1_ 

LUCSUS 

Lund University Centre for Sustainability Studies 

Artificial Intelligence for Sustainability

On theoretical limitations, practical potentials and political discourses

Annika Johanna Kettenburg

Master Thesis Series in Environmental Studies and Sustainability Science,  No 2019:027 

A thesis submitted in partial fulfillment of the requirements of Lund University International Master’s Programme in Environmental Studies and Sustainability Science 

(30hp/credits) 

Artificial Intelligence for Sustainability

On theoretical limitations, practical potentials and political discourses

Annika Johanna Kettenburg

A thesis submitted in partial fulfillment of the requirements of Lund University International

Master’s Programme in Environmental Studies and Sustainability Science

Submitted May 14, 2019

Supervisor: Henner Busch, LUCSUS, Lund University

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Abstract

Several governments recently adopted strategies to facilitate the development of artificial

intelligence (AI), one of the reasons being the pursuit of sustainability – a claim with little scientific

grounding so far. In this thesis, I strived to outline the scope of AI’s potentials by showing i) the limits

of its theoretical and practical usefulness and ii) the political barriers to using it for sustainability.

Through the method of immanent critique, I demonstrated that AI shares most of its theoretical

limitations with statistical methods in general and concluded that it is best suited for the narrowly

defined area of closed, formal systems with low decision stakes. To assess AI’s practical potentials, I

conducted a systematic literature review searching for AI use cases that contribute to the indicators

of the German sustainability strategy. The resulting use cases mostly described how AI increased

efficiencies or generated empirical knowledge, yet they only marginally addressed the major

obstacles to sustainability identified by the government.

Since technological possibilities do not linearly translate to real-world adoption, I turned to the realm

of politics to unveil dominant discourses that may shape the goals and areas of AI’s use in society. As

an exemplary space of AI’s discursive mediation, I examined the German government’s national AI

strategy using critical discourse analysis. It resulted that the government prioritized discourses of

competitiveness and technological determinism over sustainability considerations. Tensions between

different interest groups were linguistically concealed, while competition and technological progress

were invoked as external threats to be faced in consensus. This strategy of depoliticization legitimizes

and naturalizes current power relations to the advantage of certain actors. The revealed primacy of

market logics further reduced the likeliness of realizing AI’s limited sustainability potentials. Yet,

alternative ways to govern AI exist and allow overcoming the constructed necessity of neoliberal

politics.

Keywords: artificial intelligence, immanent critique, critical discourse analysis, sustainability

science, critical theory of technology

Word count (thesis): 13,988

Acknowledgements

Thank you Henner, for all your advice and guidance – thesis and beyond. You helped me to stay sane

and not to lose the joy I felt in engaging in this topic. Thank you Turaj, for motivating us to ask big

questions and directing us to the full menu of answers. Thank you Chad, for taking the time to help

me understand immanent critique. Louise, I cannot stress enough how thankful I am for your wise

words, your ability to understand my messy thoughts, your capacity to turn them into constructive

reasoning. Jens, I truly appreciate to have worked with you – and thank you for encouraging me

exactly at the right time. A tremendously large thank you also to all you amazing LUMESians, for

making the past two years as bright as they were! I consider myself very fortunate to have gotten to

know you.

Table of Content

1 Introduction ................................................................................................ 1

1.1 Relevance of this study ............................................................................................ 1

1.2 Aim, research questions, and structure .................................................................... 3

1.3 Position within sustainability science ....................................................................... 5

1.4 Critical theory of technology .................................................................................... 6

2 Potentials and limits of AI for sustainability ................................................ 7

2.1 Immanent critique of AI in theory and practice ........................................................ 7

2.1.1 Literature review design .................................................................................... 8

2.2 Theory-practice inconsistencies within AI’s canon (RQ 1) ........................................ 10

2.2.1 Machine learning in practice: data and model bias .......................................... 11

2.2.2 Conclusion and outlook to supersede tensions .................................................. 15

2.3 Scope of AI applications for sustainability goals (RQ 2) ........................................... 16

2.4 Preliminary conclusion: AI’s usefulness within limits .............................................. 20

3 Politics of AI in Germany ............................................................................ 21

3.1 Fairclough’s critical discourse analysis .................................................................... 21

3.1.1 Theories informing the analysis ....................................................................... 23

3.1.2 Introducing the AI strategy .............................................................................. 25

3.2 The strategy’s discourse practice ............................................................................ 25

3.3 Key messages and proposed actions....................................................................... 26

3.4 The government’s understanding of AI (RQ 3) ........................................................ 27

3.5 Discursive strategies and linguistic features ........................................................... 28

3.6 Social and material reasons for dominant discourses (RQ 4) ................................... 31

3.7 Pathways for transformation ................................................................................. 33

4 Concluding remarks .................................................................................... 36

References .................................................................................................... 38

Annex I. Glossary of machine learning ........................................................... 49

Annex II. Review summary of literature review ............................................. 52

Annex III. Linguistic analysis of quotes .......................................................... 73

List of Abbreviations

AI Artificial Intelligence

CDA Critical Discourse Analysis

ICT Information and Communication Technology

ML Machine Learning

RQ Research Question

SME Small and Medium-sized Enterprises

List of Figures

Figure 1. Research design. ................................................................................................. 4

Figure 2. Process of designing a machine learning system ................................................ 12

Figure 3. Fairclough's three-dimensional approach to CDA. ............................................. 22

List of Tables

Table 1. Aggregated summary of literature review .......................................................... 18

Table 2. Proposed actions of the German AI strategy. ...................................................... 26

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1 Introduction

“The Federal Government will foster research into AI technology (…) so that key areas

such as mobility, our energy systems, agriculture and food security, healthcare, the

protection of resources and mitigating climate change can be organised in a more

sustainable way – both in Germany and across the world.” (The Federal Government,

2018a, p. 17)

1.1 Relevance of this study

From science fiction literature to everyday politics – the notion of artificial intelligence (AI) has seen a

meteoric career. On a daily basis, we are presented with claims of its ‘disruptive potential’ in media,

or hear about how it will ‘revolutionize’ business (Burgess, 2018). In an article in Science, Taddeo and

Floridi (2018a) elaborated how to “unlock its potential to foster human flourishing” (p. 725). Can AI

live up to such promises?

Even though there is no shared definition of AI’s boundaries, most of the current debate is concerned

with ‘weak AI’. What is at stake here is not an omnipotent machine with similar cognitive capabilities

as humans, but a software able to process large amounts of data by novel methods. The difference to

other computer programs lies in AI’s ability to learn: The tiresome work of coding every step of the

solution has been replaced by teaching software to find solutions on its own – a process called

machine learning (ML). Learning here means to recognize patterns or anomalies in data, build

mathematical models of these, and predict future distributions. In other words, ML produces

algorithms that optimize probability distributions. This is the core of AI (Russell and Norvig, 2010).

The recent breakthroughs in ML, particularly in deep learning through artificial neural networks,

enabled further advances in other fields of AI, such as robotics, computer vision, and natural

language processing. Since there is no possibility to program every scenario an autonomous robot

would encounter in complex environments, only through ML autonomous robots – or vehicles –

became realistic. Many of today’s approaches in AI have a history of six decades marked by waves of

attention and success (for AI’s history, see e.g. Ertel, 2017; Russell and Norvig, 2010).

Despite not being omnipotent per se, aficionados see in it the new tool for solving our most difficult

problems (Morozov, 2013). The US, Chinese, Japanese, French, British and German government

regard artificial intelligence as a national priority and have launched strategies to foster its adoption

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(Saran et al., 2018). On the other hand, sustainability scholars have a long history of criticizing

technologies as quick fixes inappropriate for addressing problems’ societal root causes (Arts et al.,

2015; March, 2018; Scott, 2011). They ascribe technology a narrow space of fostering eco-efficiency

in the pursuit of sustainability (Schaltegger and Burritt, 2014) or even reject purely technological

solutions by referring to ubiquitous rebound effects and the impossibility of absolute decoupling. In

fact, it has been shown that the digitalization as such – of which AI forms a peak – is prone to curbing

the wheel of consumption faster than realizing efficiency gains (Fuchs, 2008; Scholz, 2016; Wagner,

2017).

Independent of AI’s influence on our consumption behavior, its operation is not as immaterial as the

word software suggests. The hardware necessary for AI applications, ranging from microchips to data

centers, needs to be mined, produced, assembled, shipped, distributed and disposed of. Most steps

of the value chain are likely carried out in the Global South, avoiding financial costs yet causing social

and environmental damage (Ström, 2019; Taffel, 2012). In addition, the energy consumption of

information and communication technology (ICT) is growing rapidly. Belkhir and Elmeligi (2018)

estimate the energy use of ICT devices and infrastructure in production and operation will account

for about 3.3% of global greenhouse gas emissions in 2020 (p. 457).

Apart from hardware providing the required computing power, AI necessitates Big Data and

algorithms, or computer scientists more broadly. The rapid growth of these three ingredients is the

cause of AI’s recent successes (Brynjolfsson and McAfee, 2014). However, the nature of these lends

itself to advantage certain actors. Only those with access to large computer clusters, immense data

sets, and human capital can build and operate effective AI systems. This reinforces the accumulation

of capital in hands of the already monopolistic tech companies Alphabet, Apple, Amazon, Microsoft,

Facebook, Alibaba, and Tencent. They profit from a virtuous circle of feedbacks; network effects

create ‘winners take all’ markets: More data leads to better AI systems, which increases the

usefulness of a platform, attracting more users, who again create more data. Having more users also

equates to more advertising income and investors, which allows for higher spending in innovations

and acquisitions. This dynamic paves the road to the demise of competitors (Parayil, 2005; Srnicek,

2017a).

The accumulation of data also entails risks beyond economic disparities. The mere design of social

media platforms influences the behavior of millions; meanwhile targeted manipulation of content is

able to even change the result of elections (Zwitter, 2014). Due to the power Big Data and AI grant its

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owners, we risk embarking on a geopolitical arms race for AI leadership, where safety and privacy

concerns are put aside in favor of rapid rollouts (Taddeo and Floridi, 2018b). Arms race is more than

a metaphor – autonomous weapon systems show a hazard level similar to nuclear bombs (Armstrong

et al., 2013; Brundage et al., 2018; Johnson and Axinn, 2013). Apart from this dual-use risk, the

ongoing process of connecting our infrastructure in an Internet of Things multiplies our vulnerability

to hacks, viruses, and crashes (Kitchin, 2014a). While AI is not the sole cause here, it allows for

‘smarter’ devices that are able to increase their efficiency autonomously, thus providing further

justification for smart homes, factories and cities (World Economic Forum, 2017).

These material footprints and inherent risks of AI motivate to investigate AI’s adoption within

sustainability science (for a more comprehensive overview of sustainability impacts see Scholz et al.,

2018 and Sugiyama et al., 2017). While there is much debate in media on both ends of the

cornucopian and dystopian extremes, a balanced scientific assessment of AI’s potentials and

limitations from a sustainability perspective is largely missing. So far, the scientific debate is

fragmented into i) mainstream euphoria (e.g. World Economic Forum, 2017), ii) Green ICT and ICT for

sustainability (e.g. Hilty and Aebischer, 2015), iii) eco-utopian niches of technologies for degrowth

and commons (e.g. Kerschner et al., 2018; Stuermer et al., 2017; Vetter, 2016; Zoellick and Bisht,

2018), or iv) criticism of technology and ecological modernization in general (e.g. Hornborg, 2014,

2011).

1.2 Aim, research questions, and structure

My aim is thus to critically appraise AI’s potential in fostering the realization of sustainability goals.

Here, I focus on the extent to which potentials can be realized, which excludes a detailed

consideration of AI’s risks and unintended effects. Yet, this critique of AI’s potentials alone would

remain in an ideal space of scientific rationality – in the real world other logics interfere and

constrain the realization of my findings (see section 1.4). In a second step, I therefore strive to

uncover what rationales currently govern AI politics and direct its intentionality, in the case of

Germany’s AI strategy.1 How likely is a sustainable use of AI under these political rationales? Will the

pursuit of economic strength outweigh ethical concerns? Why is AI perceived as something

inherently desirable in the first place? Vice versa, if my primary research focus were to reveal the

1 I focus on the German AI strategy, since it is embedded in the cultural context and language I am most

familiar with. This familiarity may be of advantage when conducting the critical discourse analysis, the chosen method for investigating the realm of AI politics, as it emphasizes the relations between communicative events and historically evolved social practices and institutions (Fairclough, 2010).

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political motifs behind the narrative of AI’s desirability, I would have to establish first how desirable

AI truly is from a sustainability perspective. Since this has not yet been done, I had to devote

empirical work to it.

The main questions of this thesis can be

summarized as: How can AI contribute to

sustainability? Does the German government

recognize the scope of these potentials and if not,

why? Four sub-questions arise from this that

guide my research and structure this thesis:

RQ 1: Is AI theoretically able to solve real-

world problems by producing rational

outcomes?

RQ 2: To what extent can AI practically

contribute to achieving the sustainability

agenda of the German government?

RQ 3: Is the German government aware

of these theoretical and practical

potentials and limitations?

RQ 4: What reasons underlie the German

government’s AI strategy?

The thesis is divided into two broad parts, one

practicing immanent critique (RQ 1 & 2) and one extending the critique in a critical discourse analysis

(RQ 3 & 4). I here present an entanglement of solution orientation and critical research, implicitly

asking: What is AI a solution to? What barriers do we have to surmount for using AI sustainably? The

way I answer these is through critique – that is to show the limits of a line of thought (Figure 1).

Thereby, I intend to delineate the scope of AI’s applicability for sustainability (chapter 2) and, via

embedding AI into a time and space contingent political economy, point to the need of overcoming

the constraints that impede a sustainable use of AI (chapter 3).

Figure 1. Research design founded on critical realism and informed by critical theories of technology and sustainability, which results in a three steps approach to show the limits of AI in theory (RQ 1), in practice (RQ 2), and in politics (RQ 3 & 4).

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For securing the flow of arguments, I introduce each method before the respective results. Both

methods are more than academic tools – they come with a theory, a certain worldview, which I

briefly outline in these sections.

1.3 Position within sustainability science

This ambition to navigate solution orientation and critical research lies at the heart of sustainability

science (Jerneck et al., 2011; for the distinction between these two branches see Cox, 1981). It allows

transcending the classical blueprint of natural scientists defining a sustainability challenge in

positivist terms and critical social scientists joining in afterward to search for its causes in societal

power configurations. Rather, a sustainability scientist has to integrate both ways of thinking

simultaneously and when necessary reframe and expand disciplinary concepts (Jerneck et al., 2011).

However, the large differences in ontology and epistemology may amount to unsurmountable

divides – in practice, debates often remain fragmented (Persson et al., 2018).

A field of sustainability science that addresses a variant of this tension explicitly is political ecology.

Its internal debate on the duality of materiality and discourse mirrors and reformulates the same

contradictions: ecological destruction and social inequity is real, measurable and demands solutions,

while discourses construct environmental degradation or integrity, leaving little room for an

objective record of the current state. One stream argues that nature is transformed and

appropriated through capitalistic production in material ways, whereas the other points out how

nature is always discursively shaped by historical power configurations (Robbins, 2012). Yet, this

negotiation between both strands magnifies the depth of the field and presents a nuanced whole

(Robbins, 2015).

In parallel, my research intends to integrate material concerns over technology’s impact with

discursive concerns on the construction of AI as unconditionally desirable and urgently needed.

Technology is here seen as a mediator in the relationship between economic production and

environmental change. In political ecology’s terms, AI is treated as non-human nature, whose

material characteristics “may be sources of unpredictability, unruliness and, in some cases,

resistance to human intentions” (Bakker and Bridge, 2006, p. 18). At the same time, technology is

also historically, culturally, and socially constructed – AI is a “condition but also an idea, a technology

but also a story” (Robbins, 2012, p. 232). This is a dialectic relationship: interests enact and manifest

materiality and discourse while mutually being constrained and shaped by them, in short, “a co-

evolved entanglement” (ibid, p. 80).

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Via Marxism, this dialectical conception of reality gained prominence in critical social theories and

environmental sociology, paving its way into sustainability science (Islam, 2017). What is particularly

compelling about dialectics is not only its approach to thinking about structure and agency as

inherently intertwined, contingent and thus irreducible, but also the foundation it creates for

simultaneously theorizing structural constraints and the agency to transform these.

This emancipatory element I seek to incorporate by showing that technological development is not a

natural law – as implied by notions such as ‘Moore’s law’, referring to the exponential growth in

computing power. Instead, this growth is a product of human agency and thus subject to changes

and political struggles (Taffel, 2018). By denaturalizing the dominant narrative of AI as unconditional

progress, I hope to open up cognitive space for alternative ways of governing technological

development.

1.4 Critical theory of technology

Faced with a lack of action, not of knowledge or technology, what critical sustainability scholars strive

for are rational social institutions that would enable us to act collectively on the best available

information. Rational is here understood in a Hegelian tradition, meaning to guarantee freedom of

the individual while fostering the common good over particular interests in an ethical society (Beiser,

2005). However, as history has shown, we have yet to see rational institutions.2 In an attempt of an

explanation, critical theorists have pointed to the role of the societal structures and their

mechanisms in constraining and enabling social actions (C. Wright Mills, 2000). By emphasizing

perpetual constraints on rational behavior, such as the power of ideology in shaping the agent’s

consciousness, the likeliness of rational – and thus ethical and sustainable3 – behavior decreases.

Yet, the moral imperative to pursue rationality and sustainability persists, given the limited but

existent possibility of i) acquiring objective knowledge on the state of social structures and ii)

actualizing rational behavior when knowing these structures by transforming them (ibid).

2 This is not as unanimously agreed upon as the absence of effective climate change mitigation or widening

social inequalities might suggest – progressivists commonly point to the progress made so far, attributing human development of the past decades to political and economic liberalism. In this vein, current deficiencies are described as lags or market failures that require patience and adjustments – not systemic change (e.g. Pinker, 2018). 3 I here assume that rational equates to ethical in a structural sense following Hegel and further extend ethical

to include sustainable, following Boda & Faran (2018). Yet, their precise theoretical grounding for treating sustainability as objective norm still represents a niche perspective.

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Adopting a critical theorist’s perspective, technologies result to be not what they seem, are not

reducible to their function. Instead, they are mediated and transformed through the social fabric of

society – and even when equipped with the most sustainable design – their workings are submitted

to the larger political economy and culture they are embedded in (Feenberg, 2002). This approach

offers a way to resolve the tension between assuming AI has impacts on societies’ trajectory, i.e. is

an object worthy of study for sustainability scientists, and acknowledging the interwoven position of

technology in an asymmetric network of relations (ibid). By investigating how AI currently unfolds in

society (e.g. under which conditions and with what objectives it is developed) one acquires a clearer

picture of whether this setting allows for AI to realize its potentials.

To first identify this potential of AI for sustainability, the next chapter offers an appraisal in the

narrow, technical sense that is necessary to govern its use assuming the limited possibility of rational

institutions. If there were no structural constraints, would AI be able to foster sustainability?

Sustainability is here operationalized as remaining within planetary boundaries while securing life in

dignity for all – a definition also the German government subscribes to (Die Bundesregierung, 2018a).

2 Potentials and limits of AI for sustainability

2.1 Immanent critique of AI in theory and practice

To propose a convincing critique of AI’s application to sustainability problems, I glimpsed into the

depth of computer science. The approach that allowed and demanded me to do so is immanent

critique – to judge a theory by its own terms, within its premises. Immanent critique roots back to

Hegel, who developed this form of argumentation to scrutinize prior philosophies as well as to

develop and express his dialectic method of logic (Smith, 1973): By spotting contradictions, tensions

and blind spots within a system of thought, an initial theory is presented with an antithesis. Then, out

of the interaction between thesis and antithesis, a synthesis arises that incorporates both and

transcends these, further approaching truth – that is for Hegel the ultimate reality or rational

thought itself (Singer, 1983). In practical terms, the strength of immanent critique rests in the ability

to judge a theory without resorting to a set of external criteria, whose validity would need to be

established first and would risk not being accepted by the form of knowledge under critique (Stahl,

2013).

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Immanent critique comes with theoretical baggage (Isaksen, 2018). The philosophy of science it

subscribes to – critical realism – offers a way to address the above-outlined tensions both of

materiality and discourse in political ecology (Forsyth, 2001) and of a structurally constrained

rationality in critical theory (Palermo, 2007): It resolves these by proposing a stratified ontology.

Assuming a three-level hierarchy of reality, critical realists distinguish the existence of material

mechanisms and tendencies ‘out there’ (the real), their appearance in events (the actual), and the

subset of events that are observed by people (the empirical). This allows acknowledging that most of

our scientific knowledge is constructed, incomplete and mediated through experiences, while still

retaining the possibility of asymptotically approaching knowledge of the real; it thus combines

epistemic relativism with ontological realism. However, the essential implication for immanent

critique lies in the third element of Bhaskar’s ‘holy trinity’ of critical realism: judgmental rationality. It

means to rationally choose between paradigms by judging their explanatory power (Bhaskar and

Hartwig, 2008). Here, we encounter Hegel’s legacy again: he introduced objectively grounding norms,

or paradigms, by assessing their quality in handling their internal tensions (Boda and Faran, 2018;

Smith, 1973).

2.1.1 Literature review design

In light of Hegel’s dialectics, I here started from the optimistic assumption that AI can constructively

address sustainability challenges and sought to explore to what extent it can be upheld, i.e. to outline

an antithesis. I had to refrain from ‘going all the way’ and propose a synthesis – I rather pointed to

directions out of which such could arise. As Boda & Faran (2018) explain, there is not just one but

several potential syntheses originating from theoretic tensions; the dialectic process is not

deterministic. Furthermore, delineating the necessary course of critique is usually done in hindsight.

In addition, there are practical reasons for merely presenting tensions to suffice – my overarching

goal remains to show what an ideal use of AI could look like and not how to overcome problems in

AI’s theory or its practice for sustainability.

The notions I sought to challenge in my critique are twofold, addressing both the tensions within AI’s

theory and its application for the government’s sustainability agenda. First, I investigated the claim

that AI systems are able to produce rational4 results in the real world just as well as in formal systems

given that they are designed correctly. To do so, I took the most cited textbook on AI (Russell and

4 An explanation of the computer science’s conception of rationality follows in section 2.2.

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Norvig, 2010) as a representation of AI’s theoretical core and reviewed the literature of computer

science, Big Data sciences, and statistics to spot practical problems arising when applying the theory.

I focused the review on the sub-topic of machine learning, which is underlying almost all AI systems

(Winter, 2019). Due to the assumed novelty of the topic to the reader, I created a glossary (see

Annex I) and marked terms explained there in italics.

Second, I assessed if AI applications are able to support the sustainability agenda of the German

government. If the government were to foster sustainability through AI as stated (Die

Bundesregierung, 2018b, pp. 9, 17, 42), how large would the room of possibilities be, and where

would this pursuit meet limits? By not questioning the government's definition of sustainability and

its assumptions, in particular, their quest to decouple economic growth from resource consumption

through ecological modernization (Die Bundesregierung, 2018a), I sought to answer the question

immanently.

In practical terms, I systematically reviewed how each indicator of the German sustainability strategy

2030 was addressed by AI applications in the scientific literature listed on Scopus. These

sustainability indicators were derived from the sub-goals of UN Sustainable Development Goals (Die

Bundesregierung, 2018a). The goal of the review was to discover the full range of use cases that were

discussed and not to represent the research activities quantitatively. I excluded the sustainability

indicators whose goals are likely to be met without additional action and those only relating to

government spendings. Further, I subsumed closely related indicators such as premature mortality of

men and of women. I searched for (“artificial intelligence” OR “machine learning”) and terms

reflecting the respective indicator; the Scopus search strings for each indicator were documented in

the review summary (see Annex II). The choice of the respective search terms exerted a strong

influence over the results, in almost all cases I had to use a different term than the original indicator

to increase the number of relevant articles. For example, searching for the indicator ‘premature

mortality’ yielded only two results, that is why I instead changed the search terms to the major

causes of premature mortality in Germany, i.e. cancer and cardiovascular diseases (Statistisches

Bundesamt, 2018). Due to the speed of discovery in the field of AI, I included conference

proceedings. To handle the widely varying amount of results, I set up exclusion criteria, determining

what papers would be counted as relevant and listed in the review summary (see Annex II). These

were:

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1. An article was not relevant if it

a. … did not fulfill formal requirements (e.g. conferences overview only, double listing or

the same research group publishing several very similar articles).

b. … did not address the indicator or AI, just mentioned them.

c. … used ML only as research method without foreseeing any real-world application – of

the method or of the novel type of results – which would advance the respective

indicator.

2. In the case of > 100 listed articles on Scopus, I only reviewed the 100 highest cited articles.

3. In the case of > 4 relevant articles on the same use case, I only referenced the 4 highest cited

ones in the review summary (indicated there by e.g. in the in-text references).

In the following, I introduce AI’s theoretical canon as presented by Russell and Norvig (2010),

summarize the practical shortcomings and point to theoretical directions addressing these apparent

tensions. In a second step, I then present the findings of the systematic literature review and end

with a conclusion on the appraisal of AI, synthesizing both results.

2.2 Theory-practice inconsistencies within AI’s canon (RQ 1)

“We will see before too long that achieving perfect rationality—always doing the right

thing—is not feasible in complicated environments. The computational demands are just

too high. For most of the book, however, we will adopt the working hypothesis that

perfect rationality is a good starting point for analysis.” (Russell and Norvig, 2010, p. 5)

AI is built on several assumptions which have been dominating the natural and behavioral sciences

since the Enlightenment. A common operationalization of AI makes this particularly evident – AI is an

agent that acts so as to maximize the expected average utility5 of an outcome (Russell and Norvig,

2010, p. 53). To reconcile the notion of rationality or intelligence with maximizing outcomes, AI relies

on rational choice theory. To further justify the possibility of calculating uncertainty based on

probability theory, i.e. to maintain its ontology of a deterministic universe, AI necessitates to exclude

the possibility of non-deterministic randomness and to work around the contestation of

reductionism emerging from quantum physics (David Peat, 2007). Epistemologically AI refers to

logical positivism, which assumes that uncertainty is merely a result of a lack of research and that all

5 Utility here refers to the economic notion of utility as a performance measure that quantifies the usefulness

of an action in regard to its goal. Russel and Norvig (2010) do not problematize the process of setting goals (e.g. saving lives) or to calculate utilities (e.g. metrics of the average worth of a life), they merely argue its need (p. 615).

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knowledge can be derived from sensory experiences and expressed in logic. Thus, the separation

between the sciences and humanities becomes obsolete, as humanities can ultimately be reduced to

and explained by formal systems (Russell and Norvig, 2010).

These theoretical underpinnings are crucial for understanding the logic underlying AI’s approach to

bringing about change in this world. I refrain from critiquing these schools of thought here for the

reasons outlined above, despite the fact that they run counter to the philosophical assumptions that

most parts of critical or procedural sustainability science rest on (e.g. Jerneck et al. 2011; Miller 2013;

Olsson et al. 2015).

The object of my criticism is the ability of AI to produce rational, i.e. utility-maximizing, results when

applied to informal, open systems. In such real-world settings, problems arise that its theory is

unable to account for, even given technically correct execution. Here it is AI’s theory itself that turns

a blind eye to its contradictions, apparent in the biases and trade-offs outlined below.

2.2.1 Machine learning in practice: data and model bias

Data is the key to success in machine learning – the more data points, the higher the predictive

power. The recent rise of Big Data is one of the main reasons for current breakthroughs in ML

(Kersting and Meyer, 2018). However, having a lot of data does not equate to having valid and

reliable data. In fact, ML shares a substantial amount of problems with other statistical analyses, in

particular, biases in data and model selection (Barocas and Selbst, 2016).

The term ‘model’ here refers to the mathematical modeling done by the ML system, as instructed by

the chosen learning algorithms. These algorithms specify the kind of statistical analysis to be

executed. Data and learning algorithms are the two main inputs into the ML system upon which it

autonomously builds a model that generalizes the data and predicts future distributions (see Figure

2). The most common tasks of ML systems are classification and regression in supervised learning,

clustering in unsupervised learning and reward-based learning in reinforcement learning (see

glossary) (Russell and Norvig, 2010).

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Reproducing biases of the past

The bias that is most often addressed in media differs

conceptually from the ones listed below since it would

even occur when having an ideal set of representative

data: By learning from the past the software

reproduces previous power relations, including

sexism and racism – as in the case of the US

recidivism risk management system

COMPAS (see glossary) (Dressel and

Farid, 2018). However, ML is equally

suited to provide statistical proof for

showing historical (and potentially

neglected) patterns of exclusion and

inequality (Barocas and Selbst, 2016).

Unrepresentative data

Apart from biases existing in the data itself, the most obvious mistakes arise from selecting

unrepresentative samples. For example, Wu and Zhang (2016) claimed to have designed a ML system

to distinguish righteous people from criminals based on face images, supposedly “free of any biases

of subjective judgments of human observers” (p. 1). However, the data of both population groups

were not comparable: all criminal ID photos were government issued, whereas the noncriminal

photos were taken by organizations and companies for their websites. Further, they did not control

for the socioeconomic status of the noncriminal group, where about half have a university degree

(ibid). These distortions in the data likely led to achieving high predictive accuracy in distinguishing

both groups, which contradicts findings from criminology (Shoemaker, 2018).

Apart from a faulty or too small selection of data, it is at times practically impossible to get reliable

data. In sensitive areas such as medicine, one might neither have knowledge nor access to all persons

affected by a disease such as depression. Therefore, medical data is often based on voluntary

participation, and in conjunction with the propensity of people to underreport information, it

decreases the generalizability of statistical outcomes (Pannucci and Wilkins, 2010). These

shortcomings do not reduce the legitimacy of applying descriptive statistics; only when extrapolating

Figure 2. Simplified process of designing a machine learning system, highlighting the central role of the programmer. Source: own graphic.

13

from the study population to another group, problems arise (Barocas and Selbst, 2016). Yet,

prediction is precisely what ML is intended for (Agrawal et al., 2018).

Lack of transparency

The problem of unrepresentative data becomes more severe as our ability to observe a system’s

mistakes and their causes diminishes. As an example, after many successful instances of driving

safely, an autonomous car nearly produced a crash when approaching a bridge. Only after tedious

testing of the car’s software responses to various stimuli, it resulted to have mistakenly used grassy

roadsides as a guide for direction (Castelvecchi, 2016). This malfunction likely stems from being

trained on data that disproportionately often contained grassy roadsides. The underlying technique

of deep learning via artificial neural networks (see glossary) does not allow for inspection, rendering

it very hard to uncover why the model behaved unexpectedly or to even know whether there are

mistakes that have not yet appeared – an issue commonly referred to as the ‘black box’ problem

(Castelvecchi, 2016). In short, machine learning systems in general and neural networks in particular

are inherently opaque and non-transparent (Ananny and Crawford, 2018; Burrell, 2016).

The problem of neural networks’ opacity has also been explained by reference to the Clever Hans

phenomenon. It describes how the horse Hans supposedly learned how to calculate, but instead

acted upon the reactions and clues of its instructor – it reached its given goal by other means

(Lapuschkin et al., 2019). Similarly, we do not know whether an ML system learned how to classify

objects based on their decisive traits or on other information – such as using water as an indication

for ships, not the ship itself. Here, the ML system ‘cheats’ without us noticing it since standard

performance criteria do not reveal this (ibid).

Apart from lacking explicability, ML’s opacity also offers avenues for adversarial attacks (see

glossary). This denotes the modification of objects to be classified in a way that they seem

unchanged to humans but fool the neural network (Biggio and Roli, 2018; Castelvecchi, 2016). The

consequences can be grave, e.g. when manipulating malware detection systems or traffic signs to

mislead autonomous vehicles (Bradshaw et al., 2017; Eykholt et al., 2017).

Trade-offs in model selection

Furthermore, the following examples show how not only the data input determines the quality of

ML’s results but also how people actively influence it when designing the system. The problem of

attributing importance to an irrelevant variable such as grassy roadsides is called overfitting (being

14

too well adapted to the training data so that extrapolation becomes inaccurate). It is the task of the

programmer to reduce overfitting while avoiding its opposite, underfitting (to not grasp all relevant

variables). In fact, several ‘no free lunch’ theorems have been established, stating that there is no

algorithm that yields the best results in every situation (Wolpert and Macready, 1997). Trade-offs are

inherent in building ML systems. This emphasizes the importance of administering the ML system

well and reduces the likeliness of perfection in design.

In a similar vein, approaches to ‘fair learning’, i.e. reducing e.g. sexist or racist biases, can also be

seen as a type of bias. Here, a third test criterion such as gender is applied to subtract its influence on

the results. However, there are different, irreconcilable formalizations of fairness, so choosing one

over another always implies a trade-off (see section 2.2.2). Further, by reducing the influence of e.g.

gender and age on the prediction the overall performance often decreases. In addition, one can

never account for all discriminatory variables and choosing the most important ones already entails

ethical consequences (Mittelstadt et al., 2016).

Human-machine interaction

While these aspects show the necessity of trade-offs in ML system construction, people also simply

show divergent behavior and make mistakes at times. People are often involved in labeling

unstructured data as well as in ‘helping’ the system by handling low confidence units and feeding

those back into the model. Yet, a wrongly assigned label or misplaced confirmation of a false positive

can lead to substantial distortions of the model and becomes very hard to trace down (Bajwa, 2018).

Moreover, ML systems are often said to merely serve as decision support tool, with people choosing

freely to follow their advice. Though, persons are subject to automation bias – they regard decisions

made by machines as more credible than their own and show a propensity to follow automated

advice. It has been shown that in complex environments such reliance on automation produces more

mistakes than it prevents (for the numerous mechanisms behind this, see e.g. Cummings, 2004;

Skitka et al., 1999).

15

2.2.2 Conclusion and outlook to supersede tensions

These are some6 of the problems that arise from the practice of machine learning under constrained

data availability, trade-offs in model choice and the human propensity to divergent behavior. As

Covington et al. stated, “there is more art than science” in building ML systems (2016, p. 197). This

shows a central inconsistency between AI theory and practice: the divergence of human behavior

and incomplete data are essential features of the real world, yet their absence is constitutive for

well-functioning AI systems. These problems cannot be overcome with more research, only

negotiated and their severity reduced.

Apart from the reliance on rational choice theory, there are two other central entrance points for

immanent critique to expand upon. First, there are tensions arising from assuming that probability

theory can resolve the conceptual differences between applying artificial intelligence in formal

systems and the real world. By applying probability theory to all natural and social phenomena, only

the likeliest scenario is pursued and outliers, minorities or rare events are systematically neglected.

Theories problematizing probability include Knightian uncertainty, which neglects the possibility of

quantifying uncertainty due to some degree of ontological unpredictability and epistemological

ignorance (Knight, 1921). The related black swan theory argues for the failure of probability theory to

account for rare, unprecedented events and termed the use of statistics to predict in complex

domains the ludic fallacy (Taleb, 2007).

Second, tensions appear when equating rationality to maximizing utility. Within the technical realm,

it is easy to define binary targets and measures of utility such as in classifying images correctly. Yet,

utility has to be formalized accordingly in social contexts. This is particularly difficult in tasks with

high decision stakes, such as in (semi-) autonomous weapon systems, algorithmic risk assessment in

criminal justice, or decision support systems in bureaucratic processes distributing resources

(Eckersley, 2019). In the quest for formalized, universal ethics, ML scientists encountered the

pervasive impossibility theorems that had been developed by welfare economists and ethicists (for a

detailed overview see Eckersley, 2019). These indicate that in some instances there simply may not

be a satisfactory trade-off between agents’ different objectives, i.e. no utility function addressing all

requirements simultaneously.

6 This is by far not an exhaustive list; most problems related to inferential statistics also apply here,

substantially increasing the list of shortcomings (further examples include Foster et al., 2014; Hofmann, 2015; Lipton and Steinhardt, 2018; Osonde and Welser, 2017).

16

The evaluation of AI’s use in social contexts now depends on the degree of optimism one holds i)

towards quantifying uncertainty and AI’s ability to handle ethical trade-offs and ii) on the importance

we attribute to flawless ethical behavior (‘being better than a human is good enough’ or ‘even if

decisions are just as good as human ones, automation saves resources’). From this, one might

conclude that developing ethical AI is a matter of more research into uncertainty estimating agents

(e.g. Eckersley, 2019) or into finding a formalization of fairness accepted by a majority (e.g. Maxmen,

2018). However, when being skeptical of the possibility to solve the tensions outlined above, one

might downright argue against the use of AI for making decisions affecting human lives.7

In short, the limitations of relying on rational choice theory, utilitarianism and probability theory

show that AI is best applied to primarily closed, technical systems with little uncertainty over

parameters and low decision stakes. There, it is far easier to approach complete information and

mitigate data bias. The remaining area of AI’s applicability is by no means small – in the next section,

we will explore what exactly AI has been applied for and how it might contribute to sustainability.

2.3 Scope of AI applications for sustainability goals (RQ 2)

The aim of comprehensively listing use cases of AI for sustainability goals is to show the scope of its

practical usefulness, in contrast to the theoretical usefulness discussed above. What does the

scientific literature propose to use AI for? How can these uses contribute to sustainability as

envisioned by the German government?

The literature review yielded a large corpus of relevant articles, while showing a high divergence in

quantity depending on the respective indicator of the German sustainability strategy (Statistisches

Bundesamt, 2018). The indicator that was addressed by the highest number of papers was

premature mortality (3,494 papers), followed by renewable energy (320 papers) and air pollution

(153 papers). Eighteen indicators (out of 30, in total) were addressed by ≤ 13 relevant articles, i.e.

articles passing the exclusion criteria. The indicators organic agriculture and social equality were not

addressed at all. Though, the number of use cases just pointed to the existence of different

applications, without revealing anything about the impact or usefulness of these (e.g. predicting

cancer outweighs game robots in its sustainability potential). A detailed summary of the review is

presented in Annex II, referencing 261 articles with a maximum of 4 referring to the same use case.

7 Actor-network-theory might now object that there is no technical system that operates outside the social

realm and thus render the complete detachment of ML systems from life choices impossible (Latour, 2005).

17

Due to the multitude of use cases, I refrain from describing these in depth here and instead present a

highly aggregated overview in Table 1. First and foremost, the results point out AI’s prime purpose

and use: generating empirical knowledge.8 Monitor, detect, assess, model, identify, analyze, estimate

– these were the terms most often describing the use of AI, or ML more precisely. In contrast to

other statistical tools, ML offered novel ways to analyze complex data: ML was used to integrate

various data sources, such as remote sensing and field observations (Chandra et al., 2009), or to

improve the data set quality by more accurately detecting and accounting for errors, as in the case of

citizen science databases (Bird et al., 2014). Further, it allowed harnessing messy, novel data sources

due to the unprecedented ability to easily classify these, such as bird vocalizations (Lin et al., 2017) or

marine soundscapes (Lin and Tsao, 2018).

The generation of empirical knowledge in descriptive terms was only the first step in many cases; the

goal prevailed to be prediction. Especially in transport coordination (Zear et al., 2016) or energy

production (Bhandari et al., 2015), prediction of demand and synchronization with supply offered

substantial potentials for efficiency gains. For example, ML can monitor, and via smart meters also

regulate, the consumption and saving of electricity in households or industries to partially mitigate

the weather-related volatility of energy supply and demand – one aspect constituting a smart grid

(Rogers et al., 2012; Shafiullah et al., 2010).

In individual transport, AI-based systems can instruct autonomous cars or drivers to collect people

with similar destinations and choose the best route to deliver all (Liu et al., 2013). Apart from

carpooling and -sharing, ML can improve public transport quality with a higher demand orientation

and real-time information (Roulland et al., 2014) or allocate freight more (eco-) efficiently (Bakhtyar

and Henesey, 2014). Similarly, in precision agriculture, ML allowed to precisely identify and calculate

soil properties and fertilizer requirements on fine spatial scales and thus reduce excessive fertilizer

use and run-off (Chlingaryan et al., 2018).

8 Knowledge may not be the most equate term to describe the results of ML’s data processing. While in

computer science this wording is common place, philosophy has provided us with a wide range of definitions. When viewed from other epistemological lenses than empiricism, the outputs of ML systems – patterns, anomalies, classifications or regression analyses – might simply be referred to as information, not yet knowledge (Kitchin, 2014b).

18

Table 1. Highly aggregated summary of literature review results, for full review and references, see Annex II. ‘Area’ summarizes similar indicators of the German sustainability strategy; ‘AI use cases’ refers to selected and abstracted applications of AI as presented by the literature on Scopus. Use cases marked with an asterisk were only mentioned once, yet included for illustrative purposes.

Area AI Use Cases

Poverty Monitoring, estimation, classification and prediction of poverty levels; improving

the targeting of social interventions

Health

Detecting diseases from imagery or gene expression data; predicting disease

risks from electronic health records; modeling and optimizing treatments;

designing (personalized) drugs; improving the understanding of cancer,

monitoring and nudging of health behavior; analysis of obesity determinants

Education Predicting dropout risk; online learning; improving kindergarten siting decisions*

Social equality Providing evidence of gender bias; modeling migrant behavior; analyzing

corruption across cultures, industries or regions

Corporate

responsibility

Using unbiased ML in hiring*; evaluation of suppliers; minimization of supply

chains' ecological footprint; quantifying environmental impacts of product design

and suggesting improvements*; developing meat alternatives*

Energy

production

Forecasting and regulating the energy consumption of buildings, data centers,

software, water pumps and renewable energy systems; improving the

performance of renewables in hybrid systems, smart grids and micro grids;

mitigating power quality disturbances; predicting weather conditions for placing

and operating solar and wind plants; discovering new materials for renewables;

optimizing biofuel mixtures

Greenhouse

gas emissions

Mapping of biomass carbon stocks; estimating emissions of production sites;

analysis of national emission inventories*; estimating geologic storage potential

for carbon sequestration; benchmarking tool for international climate

negotiations*

Transport

Optimizing public transport, passenger transport, bike sharing and coupled

systems through predicting demand and best routes; finding parking lots; placing

loading stations; nudging for eco-driving in vehicles; allocating and synchronizing

freight on different transport modes

Air quality Assessing and predicting air pollution levels and sites, intelligent traffic

management, optimization of combustion processes, managing urban greening

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Water quality

Optimizing waste water treatment; modeling and predicting coastal

eutrophication; mapping and predicting soil nitrogen content; identifying drivers

of soil acidification; precision agriculture: monitoring plants, soils and predicting

their fertilizer requirements; suggesting best phosphor management plan

Nature

conservation

Mapping, modeling and predicting species and habitat distributions; predicting

population dynamics; modeling extinction risks and its drivers; identifying place

and type of best-suited conservation actions; detecting illegal fishing; mitigating

bias in citizen science data*; quantifying unprofitability of high sea fishing*

Yet, to what extent do these use cases provide answers to sustainability challenges? The primary

contribution of the above mentioned cases was to increase the efficiency of resource usage or to

generate knowledge from large amounts of data. Which indicators did the government identify that

require more knowledge or efficiency innovations? In the Indicator Report 2018 (Statistisches

Bundesamt, 2018) the following factors were mentioned: i) agriculture’s fertilizer and livestock

intensity and related landscape homogeneity, ii) high share of passenger transport in (fuel-based)

cars, iii) social inequality in terms of income, nationality and gender, iv) emission intensity of fuel and

heat production, and v) economic globalization in regard to increasing freight volume and

consumption of energy-intense goods by households (ibid). These obstacles to goal achievement

point to larger structural factors that demand a more profound transformation of society’s provision

with food, energy, and goods as well as changing institutions for fairness and equality. Gains in

efficiency and empirical knowledge through AI can contribute a small part to this larger puzzle.

Overall, there are no reasons to believe that AI opens up groundbreaking new sustainability

strategies and also none to believe that AI holds no value at all. Mostly, the contributions are

marginal – improving existing processes – and not as disruptive as we may have heard others say.

Truly outstanding is AI’s ability to identify cancer cells at a higher accuracy than humans or to

autonomously navigate a vehicle in traffic.9 Yet, identifying cancer is not the solution to preventing

diseases and living well; equally, autonomous cars do not per se reduce traffic volumes or motivate

people neither to share rides nor to cycle.

9 However, there are serious doubts over the timescale and even possibility to operate fully autonomous

vehicles in complex environments such as cities (Häberli and Müller, 2018). In addition, the abovementioned central steering of autonomous cars to efficiently coordinate traffic has been declared as impermissible by the German Ethics Committee for Autonomous Driving due to the high risks of targeted manipulation (BMVI, 2017).

20

Now, in keeping with immanent critique, one could move on to propose paths to supersede the

resulting tensions of the government’s claim to use AI for its sustainability agenda. Before, one may

need to enrich the evidence base presented; I only assessed to what extent potential benefits can be

realized, leaving out the costs, risks and unintended effects that would accompany these

applications. A clear understanding of both sides would correspond more closely to a cost-benefit

analysis, a common method of choice the German government employs (Hanusch, 2011). Given AI’s

footprint outlined in section 1.1, the prospect of AI’s usefulness for the German sustainability agenda

might further decrease.

Next, one may point out an underlying inconsistency: By using these particular methods and

strategies, the government pursues a weak type of sustainability (Solow, 1991), despite explicitly

referring to planetary boundaries and social justice in their definition thereof. As scholars have

shown, weak sustainability is an insufficient approach to recognizing absolute limits to capital

conversion (Daly, 1993) and to allowing for just decision making (Sen, 1999). This would ask for a

different national sustainability strategy, prior to considerations on using AI.

2.4 Preliminary conclusion: AI’s usefulness within limits

All in all, it results that AI is neither fully rational nor most useful in addressing sustainability

challenges. Yet, it is well suited to maximize efficiencies in the technical realm and to process higher

amounts of data for aggregating empirical knowledge. In short:

ML systems are never objective and often flawed, just as other statistical analyses and

computer programs

An inherent feature of deep neural networks is opacity, impeding explanation and

accountability

ML systems cannot behave ethically since ethics cannot be reduced to one universally

accepted mathematical formula

AI can contribute to achieving the German sustainability goals through increasing eco-

efficiencies and generating knowledge

Only to a very limited extent is AI able to address the profound structural causes for current

sustainability deficiencies

Now, how come we are presented with claims of AI’s revolutionary potential and disruptiveness on a

daily basis? How come the German government devoted an entire strategy and a budget of 3 billion

euros until 2025 to foster its adoption? If it is not sustainability AI is good for, what else is it? I will

answer these questions in the second half of the thesis, by means of critical discourse analysis.

21

Technical possibilities and social mediation

Before proceeding, it is important to understand the nature of the investigation I presented here.

Since I exclusively analyzed the scientific literature, my conclusions leave out the large arena of how

society currently uses AI or its precursors, hence the current, real-world application of AI.10 How is

and will AI be mediated in society? How will we make use of automation – for flipped classrooms or

distance learning, for improved working conditions or job cuts, for more personal assistance or fewer

doctors? These questions are subjected to different logics than technical possibility.

It would exceed the scope here to list use cases of AI employed in Germany from which one might

infer common logics underlying these uses, such as profit maximization or welfare orientation.

Luckily, critical theory of technology points to the nature of these logics as being shaped by broader

sociopolitical structures (Feenberg, 2017). As Feenberg (2002) described, hegemonic interests are

manifested in the design and use of technologies via “technical codes” (p. 15). Thus, the logics

underlying AI’s uses are likely to coincide with the reasons influencing the formulation of the

government’s AI strategy; the discourses the government (re-)produces may also shape the areas

and rationales of AI’s adoption. Thus, chapter 3 will at least present an indirect answer to the

question of AI’s social mediation.

3 Politics of AI in Germany

3.1 Fairclough’s critical discourse analysis

In the third step of my inquiry, I turn to the realm of politics. My aim here is to understand the role

the government foresees for AI and to single out reasons that motivate the government to pursue

certain strategies and not others. These reasons might constrain a sustainable use of AI and hint at

entry points for transformative change. I focus on the government as a candidate that mirrors

10

Throughout the literature review, I assumed that science is an arena where technological possibilities are explored in the absence of any instrumental calculus, such as betting on the highest research funds or on marketable results. This ideal-type notion of science does not match with reality though. For example, articles within Big Data science in healthcare have been categorized as belonging to different discourses, whose adherence correlates with systematically different, up to opposing, research results – emphasizing the ideological influence of and over scientists (Stevens et al., 2018). Yet, I remain with regarding science as an area where the influence of hegemonic ideologies on AI’s design is at least smaller than in marketed AI products, for the practical reasons outlined in section 1.4.

22

dominant discourses while also being able to change these (Burton and Carlen, 2013; Vaara et al.,

2010); regarding it as a showroom of the discursive power dynamics that mediate AI’s use.

Discursive power is here understood as a force of social causation via ideological framings. Text,

speech, communicative events or discourse more broadly have an effect on the recipients and, if it

contributes to maintaining unequal power relations, this discourse is ideological. Ideologies thus

serve power; they do so through inadequate representations or explanations of our world which

justify a particular configuration of power. These ideologies manifest themselves in discursive events

– hence, treating texts as the unit of analysis as practiced in critical discourse analysis (CDA) offers a

useful method to uncover ideologies (Fairclough, 2010).

At the same time, CDA shares with critical

theory a dialectical approach to reality: texts

and social practices are no discrete entities;

they are distinct but contingent on each

other, in a constant flow of production and

reproduction. Mediating texts and social

practices, discursive practices can be seen as

an auxiliary construct to investigate how

ideologies are negotiated between the micro-

and macro-level (Fairclough, 2010, see Figure

3). In the case of the German AI strategy (the

text), discursive practices are the processes

that shape the formulation of the strategy. Social practices, in turn, present actualizations of social

structures, such as the political, economic, cultural context and historical configuration of societies.

Discourse can only be understood through an analysis of these three spheres and their

interrelatedness (Fairclough, 2010).

However, the forces that shape our discourses go beyond the realm of social practices – materiality

matters too (Fairclough, 2007). In their explanations of CDA’s relation to critical realism, Fairclough et

al. (2010, p. 220) acknowledged that “both the production and the consumption of symbolic systems

are overdetermined by a range of factors that are more or less extra-semiotic”.

Figure 3. Fairclough's three-dimensional approach to CDA

(2010, p. 59). Dotted lines indicated the openness of the

system and the dialectical relatedness of these spheres.

23

Just as discourses are shaped by the material world, they equally shape it. One might regard

neoliberalism11 as a vivid example of this material co-evolution. It has started as a scientific endeavor

of a few, a counterhegemonic discourse to Keynesianism, and now transformed the majority of our

political and economic institutions, with very material impacts (Harvey, 2005; Palley, 2004; Peck,

2008). Since these impacts have been shown to fuel a highly unequal distribution of resources to the

disadvantage of a majority and since it operates by convincing us of the normality of the status quo,

neoliberalism can be seen as an ideology – and as the most prominent object of CDAs so far

(Fairclough, 2010).

3.1.1 Theories informing the analysis

My analysis focused on the government’s economic orientation. Thereby I strived to reveal whether

the government prioritized the inscription of profit logics and instrumental reasoning more broadly12

into AI’s use (in line with economic liberalism) over the logics of orienting AI towards addressing

societal challenges (in line with Mazzucato, as I explain below). Building on the discourses resulting to

be dominant, I asked in a second step for social practices and material conditions that may explain

this discursive adherence.

I limited the scope here to economic discourses; however, this is not the only discursive sphere

illuminating how AI may be intended to be used by the government. Discursive references to

international relation theories, for example, may point out whether AI is regarded as a national

security threat motivating the government to engage in an international arms race (Burchill et al.,

2005). Though, developing autonomous weapon systems would present a clear antithesis to a

sustainable use of AI (see e.g. Shaw, 2017).

11

As an essentially contested concept, neoliberalism has several meanings that evolved and changed over time. As a political project supported by Thatcher and Reagan, neoliberalism has turned into a label for a restructuring of societal institutions characterized by laissez-faire principles. In this process, it revealed to be full of contradictions, tended to shape shifting, and functioned essentially as “a project to restore class dominance” (Harvey, 2007, p. 22). In contrast, what I refer to as ‘economic liberalism’ is an ideal-type intellectual project rooted in the economic and political theories of Hayek and Friedman. These have become part of mainstream economics today and often serve as scientific foundation for neoliberal politics (Gamble, 2006; Peck, 2008). 12

By instrumental reasoning or rationality, I refer to critical theory’s notion of domination through fetishizing means (efficiency, profit, rationality) as ends in themselves while regarding ends (e.g. participation or ethics) as means to reaching those goals (Habermas, 1968; Thomassen, 2010).

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Economic liberalism

At first glance, the German AI strategy presents a puzzle when trying to explain its existence from a

laissez-faire perspective. Accused as a “political overreaction” (S. Kooths, personal communication,

March 18, 2019), liberal economists see it as rather unwelcome state intervention. The state cannot

pick winners, they argue, since it does not possess sufficient information as to foresee the future of

the economy. Only the market is regarded as a suitable instrument for indicating the direction of

investments; competition functions as a “discovery procedure” (Hayek, 1968). The role of the state

should thus remain limited to the correction of market failures, securing a level playing field for

competition, as well as funding basic research and education (German Council of Economic Experts,

2018). So, did the German government launch its AI strategy as recognition of its task to correct

market failures – in this case, the failure to innovate at the same speed as the US and China? Or are

there other discourses at play that partially explain the government’s act of ‘picking a winner’, that is

AI? If so, is there hope then for a more sustainable governance of AI, a new model that mitigates

flaws of the neoliberal approach?

Mazzucato’s mission orientation

The German economic model, the social market economy, has traditionally sought to combine

economic liberalism with social democratic ideals and Keynesian elements of intervening and

regulating markets (Gassler et al., 2006; Streeck, 1997). In our case, a strong welfare orientation

might translate into using AI for sustainability, for addressing societal challenges. Such a mission-

oriented approach to innovation policy has been advocated for by Mazzucato. She proposed a

theoretical synthesis of Keynes, Schumpeter, and Minsky to recognize the role of the state in

fostering innovation and therefore redistribute a larger share of rewards to the state (Mazzucato,

2018a, 2013; Mazzucato and Wray, 2015). Her approach has gained prominence and is set to shape

the next decade’s EU research and innovation policy ‘Horizon Europe’ (European Commission, 2018).

Has the German government now recognized her reasoning in its AI strategy, marking progress

towards using AI for societal goals and not merely for applications that promise the highest profit?

Before investigating the underlying logics of AI’s use (section 3.5 and 3.6), I first examine the extent

to which the government appears to understand the technology along with its precise potentials and

their limits (section 3.3 and 3.4). By interrogating how the state of affairs is framed as well as what

actions are proposed, one may infer the scope of state’s perceived need for action and in parts also

the directions of action cognitively available.

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3.1.2 Introducing the AI strategy

In the past years, the political preoccupation with digital topics increased substantially in Germany. In

2013, Chancellor Angela Merkel referred to the internet as ‘Neuland’, German for unknown terrain,

which caused not only amusement and mockery on part of the public, but may also have served as a

wakeup call for the government to take on this topic (Hegemann, 2018). Since then, nearly every

federal ministry released strategies outlining how to shape and take advantage of digital

technologies (for a comprehensive overview of all initiatives see The Federal Government, 2018).

Nevertheless, politicians and citizens still perceive Germany to lag behind other countries in

digitalization, and particularly in AI’s adoption (Gehm, 2019). Therefore, the government, a grand

coalition of liberal conservatives and social democrats, issued a position paper on fostering AI’s

development in July 2018 and invited 91 stakeholders to an online consultation for their input to

finalize the AI strategy (Fanta, 2018). Among those stakeholders were 52 corporations or interests

groups of professions or industries, 16 research institutes, and only 11 civil society organizations (a

church, an environmental organization, a consumer protection agency, two unions, and six

technology-oriented associations or foundations). They were asked to rank pre-formulated

measures, yet the government’s questionnaire is not publically available (Die Bundesregierung,

2018c). Eventually, the strategy document of 47 pages13 was released on the 15 of November 2018

and received support from the opposition; viewpoints only differed on the best pathways to speed

up AI’s adoption (Deutscher Bundestag, 2019).

3.2 The strategy’s discursive practice

Since the strategy was subject to standardization and took the shape of a technical document, I will

make only a few inferences to discursive practices here. Most parts were written in a formal and

prosaic tone, characterized by long sentences, technical terms, and many repetitions. Only the

highlighted boxes on use cases along with the first summary pages were written in a more accessible

manner, mentioning many buzzwords (e.g. ‘trusted AI’, ‘ethics by, in and for design’, ‘age of AI’ or

‘gigabit society’), which may hint at public relations efforts. The marketing aspiration took on a vivid

form in the accompanying website, launched to communicate the strategy to the public (www.ki-

13

I analyzed the document in German; all citation are own translations since the English translation provided by the government showed large divergences. All page numbers refer to the German document (Die Bundesregierung, 2018b).

26

strategie-deutschland.de). Its layout and design resembled that of startups – little written content,

interactive icons, and a puristic style.

3.3 Key messages and proposed actions

The key message of the strategy can be summarized as: faced with the tremendous transformative

potential of AI, Germany has to act urgently and implement AI as widely as possible. To fulfill this

goal, a range of actions has been proposed (see Table 2). These measures often remained vague and

lacked implementation or time plans, with measures being most concrete in the support of research

and business, and less so in areas concerning regulation. Here, actions were postponed and

responsibilities shifted to various committees and dialogue platforms.

Table 2. Summary of proposed actions of the German AI strategy. The columns correspond to the main targets listed in the executive summary, p. 6 – 7.

Research for innovation and startup support Ethical guidance and dialogue

Connecting existing and opening new centers of

applied AI science (at least 12) National Observatory for AI

100 new professorships for AI European and transatlantic dialogue on ethics

Franco-German research and innovation network More training and better education for

employees

The planned German innovation agency should

have AI as a focus

Securing the influence of unions in AI’s use in companies

European innovation cluster fund for AI research 50 flagship applications of AI for the

environment and climate

20 AI trainers to support SMEs Education of and dialogue with civil society

Support of experimentation fields (where

regulation is partially lifted to allow for real-world

testing)

Roundtable with industry representatives and

regulatory authorities to establish AI guidelines

Doubling of funds for academic spin-offs

Increasing venture debt offerings in a ‘tech growth fund’

Providing incentives for data sharing and adequate

infrastructure

In addition to indicating the direction of action, i.e. to rapidly develop and adopt AI, the government

portrayed following tensions as reconcilable, implicitly suggesting we can have it all:

27

Data protection & data sharing for corporate AI systems

European cooperation & German industrial leadership

Global competition, open markets & technological sovereignty

Ethics, transparency & rapid innovation (as a goal in itself)

Thus, instead of revealing that these are mutually exclusive goal conflicts, the government framed

them as complementing elements. Conflicts were kept hidden throughout the strategy. Only at one

point in the strategy, a conflict was explicitly addressed, yet not resolved but rather avoided by vague

and general conclusions:

“The consultation and participation process (…) has been marked by two conflicting lines

of argument: on the one hand, there is concern that more stringent regulation could

potentially hamper investment, on the other, there are calls for regulation to address

non-transparent AI decisions. (…) we regard as most important the request to review

and, if necessary, adapt the legal framework (…) and the request to ensure that AI systems are transparent, predictable and verifiable.” (p. 37, emphasis added)

3.4 The government’s understanding of AI (RQ 3)

Understanding of AI’s theory

The here presented optimism towards ensuring transparency might indicate a limited understanding

of machine learning by the government. While they emphasized multiple times the need for high-

quality data as well as for the appropriate computational infrastructure, i.e. the preconditions for AI,

the internal processes and methods of AI seemed to be less understood. For example, it remained

unclear whether the government understood that machine learning is an overarching category, of

which artificial neural networks form part of, or that ML’s task speech recognition is a special type of

pattern recognition. Rather, it listed these four notions as separate fields of AI research, by stating

that “pattern and speech recognition, machine learning, [and] artificial neural networks (…) are

attracting the most attention” (p. 12). In addition, the government’s definition of AI is overly broad,

containing e.g. “knowledge-based systems: methods to model and gather expertise” (p. 5), which

could theoretically refer to empirical sciences in general.

Moreover, the government referred to the transparency of AI as if it could realistically be attained –

soon even, when provided with enough funds for further research (p. 13). Yet, the inherent setup of

neural networks determines that they are, and will remain, opaque (see section 2.2 and glossary).

Equally, the government did not show awareness of the fact that AI systems cannot be ethical. The

term ethical was used as if it was a trait one could simply assign AI to have (“ethics by design”, p. 41).

28

Also, there was no definition of what ethical would exactly mean. Only once, ethical principles were

specified as “consistent with our liberal democratic constitutional system” (p. 9).

Understanding of AI’s usefulness for sustainability

Concerning the use of AI application for societal and ecological goals, the government proposed

numerous examples. Among them were a better understanding of diseases, personalized medicine,

analysis of satellite imaginary e.g. for climate change monitoring and risk prediction, and enhancing

the transport system. Often, use cases matched exactly with my results from section 2.3 and a

remarkable level of detail was given. For example, “[robots should be used] to combat accidents in

chemical plants or to assess the structure of buildings in the dangerous wake of an earthquake” (p.

17).

This indicates that the government has understood that there are various promising use cases of AI

for sustainability. Yet, there was no recognition of their inherent limitations or their inadequacy in

addressing drivers of sustainability problems. As I will show in the next section, the underlying goal of

using these applications seemed to consist not only in advancing Germany’s sustainable

development, but also in achieving a higher acceptance of AI among the population, and in creating

new top exports similar to renewable energies. This instrumental marketing logic was more

pronounced than the intrinsic value of using AI for sustainability.

3.5 Discursive strategies and linguistic features

In the following, I investigated more closely the function of linguistic strategies in order to underline

the point raised above – the central discursive strategy was to hide conflict and legitimize the

government’s course of action. Also described as depoliticization, this macro-strategy poses a threat

to democracy itself, as Fairclough (2010) explains with reference to Ranciere (1995): Politics is the

area of struggle between the rich and poor, between oligarchy and democracy, and it is precisely this

process of negotiating conflictual power relations that prevents the oligarchy from excluding the

public. While depoliticization is as old as democracy, it has taken on a new severity in recent years

(Fairclough, 2010).

In order to hide conflict, the government often remained vague in their statements, as outlined

above. Characteristic of political discourse (Loughlin, 2002), many buzzwords and beneficial

applications were mentioned, yet no strategic direction was given – a mere list, not a ranking or

29

evaluation. Complicated or contentious issues were thereby postponed while transmitting that the

government is aware of them and in the process of handling them, so as to reduce the need for

additional action. Potential concerns were listed in the language of the ones concerned and then

linguistically treated as if the government had resolved them:

“Associated with the manifold opportunities of AI-based innovations is a responsibility

to keep track of potential risks, such as rising demand for energy, rebound effects or

resource security. Therefore, the Federal Government will step up research into AI

technology and data-based applications for a socio-ecological change and develop

criteria for assessing the environmental impact of AI as a basis for environmentally-

friendly applications.” (p. 20, emphasis added)

‘Rebound effects’ and ‘socio-ecological change’ are terms of the environmental community; they

appear in the strategy document only once – here, located in a highlighted box on the potentials of

AI for the environment. In the second sentence, the government showed that they are addressing

the problem (‘therefore’), yet all they announced was to provide a foundation for monitoring AI’s

impacts, not mitigating these. Even this notion of an assessment of AI’s impact was directly followed

by the repeated call for AI applications, which is redundant given its mention in the first part of the

sentence. This downplaying of concerns and highlighting of beneficial uses can be regarded as

legitimization efforts more broadly (for more examples of linguistic features, see Annex III).

Another example shows how conflicting views were listed without acknowledging or resolving them,

and in addition how ethical guidance and participation were often framed in rather instrumental

terms:

“A lack of knowledge and acceptance on the part of the general public could, following

the assessment [of the online consultation], impede the development and dissemination

of the technology in Germany and hamper investment. (…) Ultimately, the societal

relevance of AI development should be ensured via appropriate participatory measures.

Here, civil society actors in particular are opposed to public relations efforts which are

merely intended to foster public acceptance and demand an active involvement and co-

design.” (p. 43)

The possibility of a conflict between public and corporate interests remained neglected here, despite

apparently having perceived a need to ensure ‘the societal relevance’ of AI. In the first part, the

government argued for paternalistically ‘educating’ the public on AI, but ended with a call for active

co-design – again avoiding taking sides. Further, the first sentence on the risk of public dissent

hampering investment was not attributed to any specific actors, implying that the government

30

shared this fear. In contrast, the last sentence emphasized that only ‘civil society actors’ expressed

the demand for co-design.

This priority of convincing rather than listening to the public became more obvious at another point,

stating that “to raise the standard of research, development, and application of AI to a world-leading

level, AI needs to be viewed, desired and shaped as an opportunity. This requires (…) possibilities to

co-design the future.” (p. 45). This instrumental rationale also underlay calls for ethics and

transparency: “Technologies designed to guarantee the high European data protection and privacy

standards strengthen the trust of citizens in new AI technologies and can therefore present a

competitive advantage for German and European companies internationally.” (p. 16). In both

instances, competitiveness was framed as the highest goal.

This duality of ‘German and European’ pointed to another arena where instrumental reasons were

linguistically revealed – the strategic framing of international cooperation. European cooperation

was said to be desirable for establishing a common ethical and regulatory framework in order to

market “European AI” (p. 20) on a global scale. The nucleus of such cooperation should consist in a

Franco-German network where data, infrastructure, and ideas should be shared – presenting “the

only way for us to reach the scale that is competitive at an international level” (ibid). It remained

unclear how this quest for cooperation should not conflict with the pursuit of German leadership –

“We want Germany to build on its strong position in Industrie 4.0 and to become a world leader in AI

applications in this field” (p. 8) (for critical analyses of the term Industry 4.0 see Fuchs, 2018;

Kaufmann, 2016; Pfeiffer, 2017).

Lastly, a semiotic and perhaps ontological revelation pervaded this strategy: the invocation of AI’s

power in transforming society and the inference from technical possibilities to future developments.

This technological determinism has profound implications. It reinforces depoliticization i) by

attributing agency to objects and distracting from interests and ii) by transmitting the sense of

necessity in face of an external threat, an impersonal enemy, that Germany has to face united in

consensus. It suggests that there is no alternative than to adapt to AI and adopt it. Often paragraphs

started with the description of external changes, not attributed to any place, time or actor, until a

few sentences later the first agent appeared: ‘we’ or ‘the Federal Government’ who “will take on the

political mandate that results from the rapid advances in the field of AI” (p.6). In Fairclough’s (2010)

words, they locate the political economy of AI “within the ‘realm of necessity’, and therefore outside

31

the ‘realm of contingency and deliberation’, i.e., outside the realm of politics, semiotically realising

the macro-strategy of depoliticisation” (p. 249).

Concluding the discursive dominance of economic liberalism

To sum up, I identified an overarching tendency towards depoliticization in favor of neoliberal ideas,

an indication of post-political times (Swyngedouw, 2010). In line with economic liberalism, the

strategy presented itself as a way to “help create a level playing field” (p. 35), following an approach

that is “technology-, topic-, and industry-neutral” (p. 22, 23, 25). Global industry standards for AI

were appraised since they “open up markets” (p. 39), but this task is “primarily up to the private

sector” (ibid). Science was exclusively portrayed as a service for business, provided with funding only

if projects promise to result in “marketable products” (p. 22).

However, the strategy is far from consistent. Discursive elements contradicting laissez-faire principles

were also present, such as “the Federal Government sees it as its duty to advance the responsible

and welfare-oriented use of AI” (p. 9). Similarly, the call for European cooperation and a focus on

socially and ecologically beneficial uses hinted at a mission orientation in Mazzucato’s sense. Yet, the

promotion of 50 flagship applications does not suffice to comply with Mazzucato’s understanding of

an entrepreneurial state that actively shapes markets to the benefit of its citizens (Robinson and

Mazzucato, 2019). Adding to the strategy’s inconsistency, the general emphasis on the urgency for

action was undermined by few concrete commitments (Table 2). Thus, economic liberalism cannot

entirely account for the at times contradictory and indecisive behavior of the government – calling

for caution in labeling and simplifying the government’s politics.

3.6 Social and material reasons for dominant discourses (RQ 4)

In light of a dialectical understanding of discursive co-evolution, there are historically grown social

and material reasons for why the government formulated the strategy in this way, why they

prioritized some discourses over others. Only through understanding if and how “the social order

‘needs’ the social wrong” (Fairclough, 2010, p. 238), i.e. the depoliticized consensus on the primacy

of competitiveness and technological solutions, approaches to transformation can be elaborated.

32

Hence, I will now explore theories potentially explaining the relation between the strategy’s two

dominant discourses and the social order.14

Competitiveness and global trade

The strategy’s emphasis on global competition and its discursive framing as an overarching goal has

become a dominant feature of policy documents across the globe (Peet, 2009). Following the rules of

the liberal global economy – a space where only the ‘fittest’ survive – competitiveness is evoked as

the only way to stay among the ‘winners of globalization’. Building on the theories of Porter, the

microeconomic notion of competitiveness has been expanded to regions and nations, equated with

productivity and extended to regional prosperity more broadly (Bristow, 2005). Despite being

criticized on many grounds, the discourse did not lose in popularity among policymakers. This may

relate to competitiveness’ function in legitimizing i) the delegation of responsibility to regional

authorities, ii) the output-oriented measurement of performance providing numerical guidance and

simplicity, iii) the influence of business leaders in policy making and iv) the political course of action

as a necessary way to confront the shared threat of distant rivals in the ‘battle’ of global competition

(ibid).

In addition to these functions of competitiveness in legitimizing dominant power relations, there is

also a direct and tangible link between the discourse and the social order: Germany is dependent on

– and equally profiting from – a thriving global economy due to its massive export surplus. The

manufacturing sector was and still is a strong foundation of German prosperity, which may explain

the discursive creation and focus on Industry 4.0 (Fuchs, 2018). This notion of a fourth industrial

revolution implicitly calls for speed in harnessing connected and autonomous technologies in

manufacturing to successfully compete with global rivals (Kaufmann, 2016).

Technological determinism and financial flows

Similar to global competition, technology is framed as a threat or chance, as a justification for action.

While there are many theories explaining why people are inclined to trust in numbers as quantified

14

There are far more discourses to investigate, including the depoliticized formation of consensus around expert opinions characteristic of technocracy (Habermas, 1968), the securitization of AI (Lacy and Prince, 2018) or the marketization of research in academic capitalism (Jessop, 2018; Olssen and Peters, 2005). To more profoundly understand the pursuit of prosperity as highest goal, one may trace the development of liberalism. Rooting back to at least Kant and Mill, its ideas serve neoliberals as justification for why pursuing private interests is not an antagonist but the prime means to public prosperity (see e.g. Feher, 2009; Vázquez-Arroyo, 2008).

33

objective truths that ML appears to provide (Feenberg, 2002; Poovey, 1998; Porter, 1995), there are

also financial interests involved in making us believe in the power of AI. Apart from the obvious

profiteers – the high tech industry, AI research institutes, and consultancies eager to manage change

– there are indirect accomplices, hidden in a complex net of financial flows: major investors around

the globe (Morozov, 2018a). Eventually, it is their surplus capital which fuels the ‘acceleration’ of our

technological development. Given the increased regulation of traditional financial products and the

persistence of low interest rates in the aftermath of the last financial crisis, it is primarily the highly

innovative technology sector where one still encounters skyrocketing growth rates (Morozov, 2018b;

Srnicek, 2017b).

The resulting financial strength of tech companies allows not only the rapid development of

technologies and supporting infrastructures as during the dot-com boom (materially creating the

impression of technology’s disruptive potential) (Srnicek, 2017b), but it also strengthens their

discursive influence. With the example of Google’s framing of the future, Ström (2019) illustrated

how their portrayal of technology and entrepreneurs as key to solving all problems has become

naturalized. Thereby, the discourse of technological solutions distracts from and depoliticizes the

influence of financial logics over technological design. Contrary to techno-optimist claims, these

logics radically reduce the scope of design choices to only those promising to maximize shareholder

values (ibid).

In sum, both discourses legitimize material practices that advantage economic and political elites;

thus they can be said to form a constitutive part of the neoliberal ideology more broadly.

3.7 Pathways for transformation

These illustrations have pointed at broader constituents and constraints influencing the governance

of AI. To confine AI’s use to the areas it is best suited for and to measure AI’s usefulness against

sustainability goals rather than balance sheets, I will now present alternative governance approaches

discussed in the literature.

34

Favoring reformation

When striving to combine pragmatism with sustainability, a Mazzucato inspired approach pursued by

a united Europe would offer at least incremental improvements.15 This would encompass focusing

shared resources on developing environmentally-oriented AI applications, enforcing stronger ethical

guidelines and standards for engineers, and mainstreaming the certification of AI systems. While

industry standards and ethical guidelines have already been developed and recently released (e.g.

High-Level Expert Group on Artificial Intelligence, 2019; The IEEE Global Initiative on Ethics of

Autonomous and Intelligent Systems, 2019), their ambition level and non-binding nature have been

subject of criticism (Metzinger and Krauter, 2019).

To confront the hype around AI, Fairclough’s emphasis on semiosis suggests discursive action – as a

first step, a change of our vocabulary when speaking about AI.16 The government’s key scientific

advisors administer an online AI knowledge base where they refer to AI as “learning systems”

(Winter, 2019). This notion at least implies less potency, despite still suggesting some human-like

characteristics. Another entry point is the discursive reframing of Silicon Valley companies, startups,

and venture capitalists: They are not merely a bunch of optimistic good doers, ingenious geeks or

dazzling entrepreneurs – let alone the embodiment of progress, as suggested by the broader

narrative of technological solutionism (Morozov, 2013).

However, voluntary, individualist, ecomodernist or entirely discursive approaches have so far

resulted in few successes when measured against the goals of strong sustainability. More often than

not, the persisting logics of profitability have mitigated positive effects or resulted in unintended

consequences (e.g. Greene et al., 2019). For example, the development of ethical standards might

exacerbate the attribution of agency to AI by implicitly suggesting AI can behave ethically, just as a

person. Thereby, regulators may unintentionally serve the interests of AI companies to escape legal

accountability for the failures of their products (Elish, 2019).

15

Here, I do not refer to the currently discussed mission-oriented approach by the European Commission. The Commission described their goals therewith as increasing the impact of investments but firstly as striving to “make it easier for citizens to understand the value of investments in research and innovation” (European Commission, 2018). This rationale hints at a primacy of public relation logics. 16

In fact, ‘artificial intelligence’ entered the scientific community as a marketing notion – John McCarthy was the first to use this term in his application for research funds to the Rockefeller foundation in 1955 (Borchers, 2006).

35

Favoring transformation

For more profound solutions we need to ask: How to have prosperity by other means, which do not

require a focus on short term profitability at all costs? The problematic dynamics of digital capitalism

are not new – monopolization, commodification, privatization, and rent-seeking are inherently linked

to capitalism itself (Harvey, 2014).

In an attempt of outlining alternatives, I would like to hint at three approaches that leave behind the

rigid assumptions of competition and commodification (unlike Zuboff (2019), for example). One

approach is to treat tech companies as public utilities (Lawrence and Laybourn-Langton, 2018;

Srnicek, 2017a) or to publically own platforms (Srnicek and Butler, 2017). This is promising in cases

such as regional AI-assisted traffic systems but becomes harder to imagine once the platform’s

spatial scale increases (ibid). Another complementary approach is to change the ownership regime of

data. As the success of major platforms essentially rests on data the public provided, we are entitled

to formally own it, access it, redistribute it and negotiate its use (Mazzucato, 2018b; Morozov, 2015).

While these approaches have started to enter politics (SPD, 2019; Warren, 2019), only data

protection advocates like Stallman (2018) dare to challenge the paradigm of collecting our data on

platforms in the first place. This would impede AI development, but as shown in section 2.2, building

AI systems on personal data is problematic for many reasons. While this approach offers the most

profound solution pathway, it is also the least realistic, since personal data and the capacity to collect

it have become one of the most valuable assets this world’s largest companies possess (The

Economist, 2017).

As there is little clarity on whether and how to achieve these goals, I end with two ‘old’ principles of

effectively confronting capital: i) preventing privatization, nowadays hidden under the guise of the

smart city, and ii) protecting, adapting and enforcing our constitutional rights (Morozov, 2018a).

Instances of remunicipalization (Beveridge et al., 2014), bans of Uber across Europe (Thelen, 2018),

and the adoption the European General Data Protection Regulation (Karaboga, 2018) show that

there are reasons to remain hopeful.

36

4 Concluding remarks

Conclusion

In this thesis, I investigated the scope of AI’s potentials. Upon encountering numerous limitations, I

concluded that we are best served to employ AI only in the narrowly defined area of closed, formal

systems with low decision stakes. Furthermore, AI has mostly been developed to increase efficiencies

and find correlations, not promising any transformational change towards sustainability. Yet, an

appraisal of AI is contingent on the areas and goals AI is eventually used for, i.e. its social mediation.

Therefore I investigated what reasons motivated the German government in their national strategy

to foster the adoption of AI. It resulted that the government saw itself confronted with

contradictions and in mitigating these they chose to favor discourses of competitiveness and techno-

optimism over sustainability considerations. Yet, alternative ways to govern AI exist and allow

overcoming the depoliticized consensus on the necessity of neoliberal policies.

Methodological shortcomings

There are several limitations to my approach and methodology with one standing out – the emphasis

of broadness over depth. Intending to indicate both how to appraise AI from a sustainability

perspective and how to govern its use to foster sustainability was ambitious, yet necessary. Without

the assessment of AI in chapter two I might have been blinded by commonplace rhetorics on AI, in

particular, its attested novelty, rationality, ethics and transparency. Vice versa, without an

investigation of the drivers of AI politics, I might have overlooked the tendency of AI to function as a

vessel of capital’s interests. This limitation matters nevertheless; what I miss here is to more

convincingly emphasize the historical embeddedness of AI and its co-evolution with an economic

arrangement that reveals the regenerative power of capitalism.

Furthermore, there is a mismatch between the results each section produced: While the theoretical

critique of AI raised high doubts over using AI for influencing social behavior, the use cases

mentioned in the literature encompass applications intending precisely this (e.g. targeted at fighting

obesity or predicting poverty). This incongruence may be related to the different thematic foci,

academic affiliations, and research paradigms of the respective authors (Becher and Trowler, 2001).

Similarly, in the critical discourse analysis, I draw on authors who rarely pointed to shortcomings of

AI. Accepting AI as given, they theorized how capitalism led to its emergence. This may explain why

37

most of their solution approaches did not challenge the necessity of AI and data collection in the first

place.

Relation to sustainability science

These gaps between bodies of literature point to the main contribution of my thesis – to arrange

existing knowledge in a novel way, connect epistemic communities with different ontological

assumptions and relate them to the project of sustainability. This navigation of interdisciplinarity by

pluralistically bridging positivist and critical, realist and constructivist positions lies at the core of

sustainability science (Jerneck et al., 2011; Persson et al., 2018).

In political ecology’s theme of materiality and discourse, this bridging is central for establishing both

the material agency of non-human nature and the discursive power of hegemonic interests. I here

illustrated the agency of AI by showing i) its ‘unruliness’ in resisting to produce rational outcomes

and ii) its collaboration in a quest for control and automation. But overall, I observed how attributing

agency to AI coincided with powerful interest – constructing AI as an object to be held accountable,

as an asset worth investing in and as a distraction from the dynamics of power underlying its design.

I here intended to contextualize AI as a tiny piece in a puzzle of dialectic relations between human

and non-human nature, discourse and social practices, capital and labor, subject and society.

Sustainability science has taught me that there is no such thing as ‘just a thing’ – every object, every

phenomenon, every process is embedded in and co-evolved with natural and social systems. This

allows understanding digitalization and environmental problems as symptoms, rather than as the

objectified givens constructed in tales of future scenarios. Underlying these symptoms are prevalent

ideologies, processes of accumulation, institutions that advantage actors unequally – and while this

emphasis on structural causes may dismiss hopes for quick fixes, it instead points to the power of

collective action as a path for transformation.

38

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Annex I. Glossary of machine learning

Adversarial attacks: Also called adversarial examples, this notion describes the targeted

manipulation of a machine learning system by modification of the objects to be classified. These

modifications are designed to escape human attention. Virtually every method of machine learning

and every data input type has been susceptible to these attacks (Biggio and Roli, 2018; Finlayson et

al., 2019).

Artificial neural networks: A broad framework of machine learning algorithms, inspired by biological

neurons (Wolchover, 2017). They are the prime method of deep learning systems and unsupervised

learning (see entries below).

COMPAS, recidivism risk management system: This system has been used in pretrial, parole, and

sentencing decisions for more than one million offenders in the US so far. As Larson et al. (2016)

uncovered, it exhibited racial bias against black people. Despite a comparable rate of predictive

accuracy (67% and 64%), COMPAS over-predicted recidivism for black people while under-predicting

it for white people. This is in parts because in the underlying data set 51% of black defendants

recidivated, compared to 39% of white defendants (ibid). What is more, Dressel and Farid (2018)

found that predictions by COMPAS were just as accurate as those of lay people. In addition, using

Figure 4. An illustration of an artificial neural network, here performing the task of recognizing the picture of a

dog. In real-world applications, several thousand layers are added with their function remaining hidden.

Source: Wolchover, 2017.

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137 features to classify defendants did not result in better results than just using two features (i.e.

age and the number of previous convictions) – putting into question the added benefit of algorithmic

recidivism prediction overall.

Deep learning: Deep learning is a sub-category of machine learning that operates on neural

networks. Deep learning methods have achieved large successes recently, such as the win of

Alphabet’s AlphaGo program over the world champion in the Asian game Go in 2016. The notion

‘deep’ hints at the hidden layers of these networks; deep learning systems are essentially black-

boxes. The only achievable transparency is to reveal the numerical weighs a network node carries,

yet these numbers are impossible to interpret (Ananny and Crawford, 2018; Castelvecchi, 2016). To

keep in mind, “deep learning systems are not testable, cannot be evaluated, change their features,

do not give explanations, are easy to manipulate, arbitrarily designed and always imprecise.”

(Laßmann, 2017, Pos. 1304, own translation)

Main types of machine learning differentiated by the feedback to ‘learn’ from:

Reinforcement learning: A type of machine learning where the model is instructed to learn from

rewards and mistakes to maximize the overall reward score over time. This trial-and-error learning

style intends to mirror the learning behavior of humans and animals. Examples include

recommendation and suggestion systems that adapt to the user’s behavior and improve over time

(Russell and Norvig, 2010).

Supervised learning: A wide range of learning algorithms used to build models on labeled data (such

as support vector machines, naïve Bayes, decision trees or neural networks). Here the model ‘learns’

to generalize from the training data to novel data by building a function of the input-output relation.

The most common tasks are classification and regression analysis (Russell and Norvig, 2010).

Unsupervised learning: Here the learning algorithms are neural networks that are able to build

models on unlabeled data. The network searches for patterns in the data, mostly to perform cluster

or principal component analyses. This renders it particularly useful for Big Data analysis, where data

is often uncategorized and messy (Burrell, 2016).

51

References

Ananny, M., Crawford, K., 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20, 973–989. doi:10.1177/1461444816676645

Biggio, B., Roli, F., 2018. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognit. 84, 317–331. doi:10.1016/j.patcog.2018.07.023

Burrell, J., 2016. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data Soc. 3. doi:10.1177/2053951715622512

Castelvecchi, D., 2016. The black box of AI. Nature 538, 20–23.

Dressel, J., Farid, H., 2018. The accuracy, fairness, and limits of predicting recidivism. Sci. Adv. 4, 1–6. doi:10.1126/sciadv.aao5580

Finlayson, S.G., Bowers, J.D., Ito, J., Zittrain, J.L., Beam, A.L., Kohane, I.S., 2019. Adversarial attacks on medical machine learning. Science (80-. ). 363, 1287–1289. doi:10.1126/science.aaw4399

Larson, J., Mattu, S., Kirchner, L., Angwin, J., 2016. How We Analyzed the COMPAS Recidivism Algorithm [WWW Document]. ProPublica. URL https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm

Laßmann, G., 2017. Asimovs Robotergesetze – Was leisten sie wirklich? Heise Medien.

Russell, S., Norvig, P., 2010. Artificial lntelligence - A Modern Approach, 3rd ed. Pearson Education, Upper Saddle River, New Jersey.

Wolchover, N., 2017. New theory cracks open the black box of deep learning [WWW Document]. Quanta Mag. URL https://www.quantamagazine.org/new-theory-cracks-open-the-black-box-of-deep-learning-20170921/#

Annex II. Review summary of literature review (see explanation of symbols on last page and bibliography attached)

Use cases

No Poverty Material depreviation

Indicator is measured in relation to European average, which is declining faster than the German one

Monitor poverty dynamics (Zhou & Liu 2019), classification of poor populations as decision support (Sani et al. 2018), estimate and predict poverty levels of regions from satellite imagery (Pandey et al. 2018, Jean et al. 2016, Xie et al. 2016), energy consumption prediction to alleviate energy poverty (González-Vidal et al. 2018), "algorithmic social intervention" for more effective targeting and delivery of government interventions (Wilder 2018), prediction models supporting social service provision (Serrano et al. 2017)

ABS ( "artificial intelligence" AND ( poverty OR deprivation ) )

42 (8)

Nitrogen surplus(2014)

Nitrogen input consists to 55% of fertilizers and 34% of animal feed, no foreseeable reduction

Precision framing: calculate optimal fertilizer dosage (Moreno et al. 2018, Chlingaryan et al. 2018), accurately estimate plant traits (Maimaitijiang et al. 2017) & predict soil nutrient content based on remote sensing (Zhang et al. 2018), ML in genetic engineering to increase nitrogen use efficiency of crops (Araus et al. 2016, Varala et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND nitrogen AND agriculture )

14 (6)

Organic agriculture No indication given

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( "organic agriculture" OR "organic framing" ) )

0

Premature mortality(women & men) (2015)

Highest share of causes for premature mortality have cancer (38 %) and cardiovascular diseases (21 %); factors influencing mortality are health behavior and medical service

Classification of tumors for diagnosis and treatment based on medical imagery or gene expression data (e.g. Dudoit et al. 2002, Esteva et al. 2017, Statnikov et al. 2005. Wang et a. 2007, Furey et al. 2000), predicting cancer susceptibility, recurrence and mortality and improve basic understanding of cancer development and progression (e.g. Cruz & Wishart, 2006, Kourou et al., 2015) e.g. identifying genetic determinants of diseases (Xiong et al. 2015), predict the future health of patients from the electronic health records (Miotto et al., 2016), predict drug-target interactions for drug discovery and personalized medicine (Chen et al. 2016)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( cancer OR "cardiovascular diseases" ) )

3,494

Obesity rate among adultsand adolescents

Obesity is caused by unhealthy nutrition and insufficient physical activity, which correlate with low socio-economic status in case of adolescents (3-4 times higher risk than in high status groups)

Automated dietary/health self-monitoring: energy expenditure estimation via smartphone body sensors (Pande et al., 2013) or monitoring via wearable systems (Pouladzadeh et al., 2015, Prioleau et al. 2017), automated ingestion detection (Walker and Bhatia, 2014), computer vision for automated food logging (McAllister et al. 2018); mobile personal trainer system (Buttussi & Chittaro, 2008), humanoid robot Health Coach (Addo et al. 2013), intelligent playware technology for physically activating play (Lund et al., 2005), recommendation-based argumentation models in public health policy (Bourguet et al. 2013), modeling and predicting obesity: big data analysis for understanding obesity (Abdel-Aal & Mangoud. 1997, Dugan et al., 2015. Dharmasena et al, 2016, DeGregory et al. 2018), and more personalized health activities (Huh, 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND obesity )

134

Zero Hunger

No results

Good health and well-being

Indicator Status(2017)

Impacts of AI on respective indicator Search string on Scopus (20-23/02/19)

# results (# rele-vant*)

Factors impeding goal achievement stated by

government

Emissions ofair pollutants (2016)

Emissions of VOCs and particulate matter were reduced as scheduled, yet the reductions were too slow for nitrogen dioxid (traffic and energy sector) and sulphor dioxid (energy sector), emissions of ammonia even increased slightly (livestock farming and biofuel production)

Environmental decision support: identifying pollution sources and predicting urban air quality (e.g. Lekkas et al. 1994, Singh et al, 2013, Yeganeh et al. 2012, Shaban et al. 2016), novel index for air quality assessment (Sowlat et al. 2011) incl. inferring data from social media posts (Mei et al. 2014, Jiang et al. 2016); intelligent transport system: autonomous traffic control for autonomous vehicles (Au et al. 2011, Zear et al. 2016) or optimizing truck waiting time at logistic nodes (Hill & Böse, 2017), optimal power management of hybrid electric vehicles (Ali & Söffker, 2018); monitoring and optimizing combustion processes (Lee et al. 1998, Sujatha et al. 2013), assessing and managing urban tree canopy and greening (Jia et al. 2013, Guo et al. 2019), smart bins sending alerts when critically filled and minimizing garbage-trucks' routes (Baby et al. 2018), power plants air pollution monitoring (Dragomir &, Oprea, 2013)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "air pollution" )

153

Early school leavers (18- to 24-year-olds)

No indication given

Early dropout warning system based on national data (e.g. Chung and Lee, 2019, Sasone, 2018, Sorensen 2018, Gomes & Almeida 2017), understanding and detecting dropout in distance learning (e.g. Isidro et al. 2018, Dalipi et al., 2018, Niu et al. 2018, Kostopoulos et al. 2018), prediction of student engagement using frontal face images (Hamid et al. 2018), online learning for personalized education (Tekin et al. 2015)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND dropouts AND ( school OR education ) )

33 (20)

All-day careprovision for children0- to 5-year-olds (2018)

Lower rate of day care provision to migrants, large difference between west and east Germany

Identifying locations currently underserved with pre-Kindergarten programs to improve allocation (Shroff et al. 2014)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( kindergarten OR "child care" OR nursery ) )

41 (1)

Gender equality Gender pay gap

75% of the gap are due to statistical reasons: less women in leading positions and more working part time or in low-paid sectors

Improving ML to reduce evidence of stereotyping (e.g. of gender) in predictions (e.g. Zhang et al. 2018, Pérez-Suay et al. 2017, Quadrianto & Sharmanska, 2017, Leavy 2016), assessing gender bias in economic sectors based on Big Data (Huluba et al. 2018, Kou 2017), introducing (unbiased) AI in the hiring process (Bots et al. 2018), Big Data for understanding gender disparities on work-related online platforms mediated by algorithms in web design (Wachs et al. 2017)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "gender" ( pay OR salary OR *equ*ity ) )

23 (9)

Phosphorus inflowing waters (2016)

Half of phosphorus run-off from agriculture, half from cities esp. wastewater treatment

Modeling of wastewater treatment plants for design and process optimisation (Gernaey el al. 2004, Tabatabaei et al. 2007, Chou et al. 2003), enhanced water quality monitoring through identifying critical sampling locations (Strobl et al. 2006) or combining in situ samples with remote sensing (Jakovljević et al. 2018), predicting the efficiency of permeable pavement in filtering storm water (Tota-Maharaj & Scholz, 2012), assessing climate change induced changes in efficiencies of phosphor management practices in rivers (Jeon et al. 2018), digital mapping of soil available phosphorus supported by AI for precision agriculture (Dong et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND phosphorus AND *water )

23 (8)

Quality education

Clean water and sanitation

Nitrate ingroundwater (2015)

Nitrate runoff from fertilizers, including commercial ones, manure and biogas residues

Predictive modeling of groundwater nitrate pollution (e.g. Rodriguez-Galiano et al. 2014, Nolan et al. 2015, Nadiri et al. 2017, Sajedi-Hosseini et al. 2018), precision nitrogen management in agriculture (Li & Yost, 2000)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND nitrate AND *water )

27 (11)

Final energyproductivity

Primary energyconsumption

Share of renewableenergies in gross final energy consumption

The goal concerning renewable electricty has been reached, yet gross final energy also includes mechanical energy, heat and fuel; no indication is given why it is harder to scale up renewables in these areas

Modeling, designing and predicting the performance of renewable energy systems (Kalogirou, 2006, Jha et al. 2017, Jaba et al. 2015) and optimizing hybrid renewable energy systems (e.g. Sinha & Chandel, 2015, Bhandari et al. 2015, Al-falahi et al. 2017, Zahraee et al. 2016), intelligent energy management of a microgrid (Chaouachi et al. 2013, Fang et al. 2011, Mallesham et al. 2012), realizing and improving smart grids (e.g. Venayagamoorthy, 2009, Shafiullah et al. 2010, Rogers et al. 2012, Liao et al. 2012), estimating daily solar irradiation for placing solar plants and forecasting outputs (e.g. Bosch et al. 2008, Ramedani et al. 2014, Salcedo-Sanz et al. 2014, Raza et al. 2016), classifying rooftops for photovoltaic deployment (Assouline et al. 2017), improving prediction of wind speed (e.g. Zhang et al. 2013, Xiao et al. 2015, Ssekulima et al 2016, Ak et al. 2016), diagnosing damage of a wind turbine blade using pattern recognition (Dervilis et al. 2014), classifying and mitigating power quality disturbances (Khokhar et al. 2015, Kow et al. 2016, Pérez-Ortiz et al. 2016), improving maximum power point tracking (Mellit & Kalogirou, 2014, Gupta et al. 2016), fouling control in biomass boilers (Romeo & Gareta, 2006), modeling hydrogen production methods (Ardability et al. 2018), discovering new materials for renewable energy (Ermon et al. 2015, Le Bras et al. 2014), optimizing mixture properties of biodiesel production (Cheng et al. 2016), forecasting national renewable energy consumption (Ma et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "renewable energy" )

320

Decent work and economic growth

Global supply chains (Textile Partnership) (2018)

No indication given

Evaluation of suppliers in supply chains (Pang, 2006, Shore & Venkatachalam, 2003), multi-agent systems to improve environmental decision making in forest industry (Frayret 2011), AI-based learning in sustainable supply chain management (Zijm & Klumpp, 2015), IoT-based supply chain monitoring of environmental and safety risks (Tsang et al. 2018), measure the carbon footprint of trade supply chains (Yu et al. 2016), traceability of products (Kim & Hwang, 2018, Yingjie, 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "supply chain" AND ( transparen* OR responsib* OR social OR safe* ) )

59 (8)

121

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "energy consumption" AND "energy efficiency" )

Primary energy consumption is related to weather conditions and economic growth; final energy productivity is measured in relation to the GDP and GDP growth did not outpace final energy consumption substantially

Affordable and clean energy

Forecasting the energy consumption of buildings (e.g. Jain et al. 2014, Deb et al. 2017, Chou & Ngo, 2016, Paudel et al. 2017), estimating the thermal diffusivity of building materials (Grieu et al. 2011), model user occupancy and activity patterns for energy saving in buildings (e.g. Peng et al. 2018, Ortega et al. 2015, Ahmad et al. 2018, Cao et al. 2016), energy prediction model for machine tools (Bhinge et al. 2017), energy efficient routing for electric vehicles (Masikos et al. 2014), adaptive power management for data centers. software and wireless sensor networks (e.g. Hankendi & Coskun, 2013, Kan et al. 2012, Chincoli & Liotta, 2018, Wu et al. 2015), minimizing electricity usage in water sector (Bagloee et al. 2018), solar power prediction for commuity microgrids (Cabrera et al. 2016), analysis and design of heat pump based hybrid renewable energy systems (Dimache & Lohan, 2011), electricity demand and population dynamics prediction from mobile phone metadata (Wheatman et al. 2016)

Foreign schoolgraduates No indication given

See indicator "early school leavers", in addition:Migrant behavior analysis based on classification of residents as native/migrant from mobile phone data (Wang et al. 2018, Yang et al. 2018), modeling social network dynamics (Yan et al. 2018), model behavior of migrant employees (Tarasyev et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( "social integration" OR migrants ) )

18 (4)

Gini coefficientof equivaliseddisposable income (2016)

No indication given

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( "Gini coefficient" OR "social *equ*ity" ) )

10 (0)

Settlement density(2016)

Decrease in density in rural areas partially due to population decline

Analysis of driving forces of urban expansion (Li et al. 2014), tracing land use change patterns through classifiying settlement types (Gounaridis et al. 2018, Jochem et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "land use" AND density AND ( settlement OR *urban ) )

8 (3)

Final energyconsumption infreight transport (2016)

Energy use increase due to shift to road transport, increase in freight volume (decrease in production steps per company) and increase in distance between production sites and place of use

Behavioral analysis of freight mode choice decisions (Samimi et al. 2011), predicting the volume of road freight (Mroẃczyñska et al. 2012), or freight transport using electronic waybills (Bakhtyar & Henesey, 2014), prediction of arrival times of freight traffic on railroads (Barbour et al. 2018), integrating logistics decisions into freight transport modeling (Piendl et al. 2019), optimizating train schedules of freight trains (Rößler et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND freight AND ( transport OR traffic OR rail ) )

26 (6)

Final energyconsumption inpassenger transport (2016)

Both passenger transport volume and efficiency (predominatly in train sector) increased and balanced each other, no improvement foreseeable; passenger transport consists to 84% of cars or motorcycles, which are used for work related transport with increasing frequency

Intelligent transportation systems: managing electric vehicles in the smart grid (Rigas et al. 2015, Kahlen & Ketter, 2015, Lopez et al. 2018), placing loading stations for electric vehicles (Funke et al. 2014), autonomous vehicle management (Ionita, 2017), finding parking lots (Alsafery et al. 2018, Mago & Kumar, 2018); optimizing public transport systems: evaluating unconventional funding (Ubbels & Nijkamp, 2002), analyzing success factors (van Egmond et al. 2003), predicting impact of fare adjustments (Tu et al. 2016, Weng et al. 2017), eliminating Bus Bunching (Moreira-Matias et al. 2016), improved planning (Molina, 2005, Mackett 1994), analyzing public transport acceptance (Raflesia et al. 2018), design of public transport information service (Yu et al. 2018), identifying optimal transit stations (Prashanth et al. 2016), modeling demand from public transport fare collection data (Roulland et al. 2014); modeling travel mode choice (Hagenauer & Helbich, 2017), optimizing bike sharing systems (Singla et al. 2015, Lin et al. 2018), Human machine interaction in vehicle: promoting eco-driving behaviors (Di Lena et al. 2017), advising for energy saving in climate control (Azaria et al. 2014), optimizing solar car speed based on irradiance (Shao et al. 2016)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "public transport" OR ( "car" AND "energy" ) )

89 (24)

Accessibility of public transport

not quantified No indication given Generating and preprocessing of geospatial data for public transport network design

(Gobulev et al. 2016)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "public transport" access* )

3 (1)

Reduced inequalities

No results

Sustainable cities and communities

Products with verified eco-labels (2016)

No indication given

Metamodel to support a building labeling program and provide design guidance (Racket el al. 2016), introduction of quality labels for promoting computational sustainability in AI / ML technologies (Thelisson, 2018), increase tracebility of wood products to faciliate certification (Kim & Hwang, 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( certified OR certification OR label* ) AND ( sustainab* OR green ) )

42 (3)

Energy consumptionand CO2 emissionsfrom private householdconsumption

(2015)

39% of all emissions are direct ones, from the use of electricity, heat and fuel, 61% are related to consumption of goods. Overall, the highest share of emissions stems from living, transport and diet

See indicators of "clean and affordable energy" for living and mobility, in addition: Quantifying the environmental impact of product design decisions (Wisthoff et al. 2014) and automating the conceptual design to minimize products' environmental impacts (Haapala et al. 2011), ML designing plant-based meat alternatives (Schmidinger et al. 2018), improving LCA to optimise production processes e.g. of chicken (López-Andrés et al. 2018, Li et al. 2018), classifying social networks for challenging its group members to reduce their energy consumption through gamified competition (Saffre et al. 2011), product return prediction in e-commerce (Zhu et al. 2018), identifying consumer decision journey stages on social media (Vázquez et al. 2014), demographically characterising vegetarians (Lusk, 2017), behavior changes in shopping with a robotic companion (Bertacchini et al. 2017), assessing the use-phase energy consumption of consumer electronics (Mashhadi & Bedhad, 2017)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( "vegetarian*" OR "meat consumption" ) OR "shopping behavior" OR "carbon footprint" OR "life cycle assessment" )

56 (11)

EMAS eco-management No indication given ML to help environmental managers identify significant environmental aspects

(Bates, 2002, Ghose et al. 2009)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( "environmental management system" OR ( "environmental management" AND business ) ) )

6 (2)

Climate action

Greenhouse gasemissions

The overall emissions are to 84% comprised of carbon dioxid, which mostly occur in electricity and heat production

See all energy related indicators, in addition:Mapping of biomass carbon stocks using remotely-sensed data e.g. in tropical forests or mangroves (Mascaro et al. 2014, Urbazaev et al. 2018, Pham et al. 2019, Roxburgh et al. 2019), autonomous materials discovery for clean energy (Tabor et al. 2018), assessing rooftop photovoltaic potentials using geodata and image recognition (Mainzer et al. 2017), estimating greenhouse gas emissions of production sites, e.g. fattening farms or water treatment (Hosseinzadeh-Bandbafha et al. 2017, Porro et al. 2017), estimating geologic storage potential for carbon sequestration (Jonsson et al. 2014), sectoral performance analysis of national emission inventories (Ganzenmüller et al. 2019), integrating waste management companies in micro grids (Graus et al. 2018), predicting power for home appliances based on climatic conditions (Kaur & Bala, 2018), computing a benchmarking tool for international climate negotiations (Prados et al. 2015)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND "greenhouse gas emissions" OR "climate change mitigation" )

40 (13)

Responsible consumption and production

Nitrogen input via the inflows into the North and Baltic Sea (2016)

No indication given

Modeling and predicting coastal eutrophication and algae blooms (e.g. Melesse et al. 2008, Kehoe et al. 2012, Jiang et al. 2016, Volf et al. 2011) incl. understanding its predictors (Tamvakis et al. 2012), model and map soil loss laden with nitrogen by water erosion (Teng et al. 2016)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( ( nitrogen AND sea ) OR eutrophication ) )

22 (9)

Share of sustainablyfished fish stocksin the North and Baltic Sea

(2016)

Statistical problems in measuring the indicator, fishery is international and cannot be controlled by one state alone

Classifying and predicting fish species distributions and population dynamics (e.g. Suryanarayana et al. 2008, Fernandes et al. 2010, Pittman & Brown, 2011, Leow et al. 2015) incl. invasive species (Coro et al. 2018), identifying habitat preferences (Džeroski & Drumm, 2003), analyzing bycatch patterns (Oliver et al. 2015), quantifying how limited the cases of profitable high sea fishing were without state subsidies (Sala, 2018), uncovering latent research topics in fishery models (Syed & Weber, 2018), agent-based modeling within the socio-ecological systems framework (Cenek & Franklin, 2017, Hayes et al. 2011), detecting illegal fishing ships (Sutikno et al. 2018, Haskell et al. 2014) incl. their fishing gear (Marzuki et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND fishery )

68 (45)

Species diversity and landscape quality (2015)

Overall, there has been an ongoing decline, sub-index forest perfomed best at 90 (target for 2030 = 100), worst were sub-indeces of farmland and coasts / seas at 59

Modeling, mapping and predicting species and habitat geographic distributions (e.g. Peterson, 2001, Rangel & Loyola, 2012, Vaglio Laurin et al. 2014, Pouteau et al. 2012), incl. under climate change scenarios (Bitencourt et al. 2016, Walsh et al. 2018) or based on soundscapes (Lin et al. 2017, Lin & Tsao, 2018, Maina, 2015), decision support for conservation planning handling trade-offs (e.g. Chandra et al. 2009, Urli et al. 2016, Farrell et al. 2011, Rodríguez-Soto et al. 2017), detecting drivers and hotspots of extinction risk and predicting its level for species (Davidson et al. 2012, Bland et al. 2015, Lughadha et al. 2019, Pelletier et al. 2018), assessing spatial habitat heterogeneity and land cover (Fukuda et al. 2015, Jones et al. 2018, Kelley et al. 2018), classifying the health of individual trees (Shendryk et al. 2016), handling error and bias in global citizen science datasets (Bird et al. 2014), identifying variables causing changes in monetary estimates of biodiversity (Nijkamp et al. 2008), reducing uncertainty in global biodiversity indicators (Bland et al. 2015), biomonitoring through DNA-generated, trait-based food webs (Compson et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND biodiversity AND ( conservation OR protection ) )

58 (46)

Eutrophication ofecosystems (2015)

Worse in northern than southern Germany due to higher share of agriculture

See indicators "nitrogen surplus", "emission of air pollutants" and "nitrate in groundwater", in addition:Predicting and mapping nitrogen content in soil (e.g. Morellos et al. 2016, Forkuor et al. 2017, Hengl et al. 2017, Martínez-España et al. 2019), estimate leaf nitrogen accumulation at canopy level (Cui et al. 2016, Tan et al. 2018), identifying drivers of soil acidification (Wang et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND ( nitrogen AND ( atmospher* OR soil ) AND NOT agricultur* ) )

33 (7)

Peace, justice and strong institutions

Corruption PerceptionsIndex in Germany / partner countries

No indication given

Improving efficiency, transparency, and halt corruption in governments through technology use incl. AI (no effects of AI on corruption proven so far) (Valle-Cruz et al. 2015), analyzing corruption across cultures, within industries or mapping it across regions (Gal et al. 2014, Łatek et al. 2010, Noerlina et al. 2018)

ABS ( ( "artificial intelligence" OR "machine learning" ) AND corruption AND government )

7 (4)

Life on land

Life below water

If the trend continues, the indicator will foreseeably miss its target by at least 5 % and at most 20 % of the difference between the target value and the current value

The indicator is developing in the right direction, but if the trend continuous the target value will be missed by more than 20 % by the target year

The indicator is not developing in the right direction and therefore the gap to the target value is widening

Source: Federal Statistical Office (2018): Sustainable Development in Germany, Indicator Report 2018

59

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Annex III. Linguistic analysis of quotes

Quotes from the AI strategy (Die

Bundesregierung, 2018b, own translation) Linguistic analysis and interpretation

Technological determinism and the power of AI

The Federal Government will take on the political

mandate that results from the rapid advances in

the field of AI and will make comprehensive use of

the push for innovation accompanying the

technology for the benefit of all. (p. 6)

AI is portrayed as inherently powerful and from

this attributed power the government infers a

need for action. This action should come in the

shape of innovations, without specifying their

purpose, and this is regarded as beneficial for

society at large. Conflicts about this situation-

means-goal relation remain hidden.

AI has reached a new maturity stage over the past

years and, as a basic innovation, is becoming the

driver of digitalization and autonomous systems in

all areas of life. (p. 9)

By referring to the maturity stage (Reifephase,

literally ripeness phase), comparisons are invoked

to natural systems. No agents are mentioned, no

interests, no economic reasons; merely the traits of

AI are said to responsible for this process of AI’s commercialization and diffusion.

The adoption of AI will lead to a new stage of

change for work, with clear differences to previous

automation and digitalization. (p. 25)

The uniqueness of AI is declared without any

reasoning or empirical proof to substantiate it. My

results give little support for massive qualitative

differences compared to prior technologies.

Horizontal technologies like AI will touch all fields

sooner or later. This development is global,

therefore politics has to think and act globally too.

(p. 42)

The rationale for global cooperation is founded on

technical specifications of AI. It is suggested that

politics should mirror the presumed globalism of

AI; globalization itself is equated to technical

possibilities. In fact, AI is not global; it is very

specific to the data it was trained on and often not

easily transferable.

We are exploring the possibility of a new Workers’ Data Protection Act that would protect employees’ data in the age of AI. (p. 28)

The metaphor of an “age of AI” clearly points to an overemphasis of AI’s transformational potential. In addition, the government talks about ‘exploring possibilities’ and not about actual measures.

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Instrumental reasons for ethics and dialogue

A holistic, human-oriented and user-centric

approach is essential for the development and

positive use of AI in the workplace and a

precondition for exploiting AI’s innovation and productivity potentials. (p. 25)

A collection of buzzwords (often used yet never

specified) is meant to show the government’s commitment for an employee-friendly use of AI.

Yet in the second part of the sentence, the

rationale for user-centricity is stated to be

economic in nature.

At present, AI is perceived as controversial by large

parts of the population. To raise the standard of

research, development, and application of AI to a

world-leading level, AI needs to be viewed, desired

and shaped as an opportunity. This requires

intensive societal dialogue, participatory processes

and possibilities to co-design the future. (p. 45)

The sole reason for engaging with citizens seems to

be the need to catch up in global competition. The

public is paternalistically portrayed as subjects

lacking information on how to evaluate AI

correctly. Participation is only desired in so far as it

contributes to AI’s adoption and acceptance.

Technologies designed to guarantee the high

European data protection and privacy standards

strengthen the trust of citizens in new AI

technologies and can therefore present a

competitive advantage for German and European

companies internationally. (p. 16)

Not trust but the competitive advantage is

presented as the final goal. In addition, legal

compliance should be obvious and not worth

mentioning.

We will make use of the opportunities AI offers for

the healthcare sector and support the use of data

from distributed sources, in conformity with data

protection law and taking account the interests of

patients worth protecting. (p. 18)

Apparently, not all interests of patients are worth

protecting. A reason for this emphasis could be

interpreted against the backdrop of the general

reluctance of Germans to share personal data: the

health tech sector would appreciate governmental

support in acquiring the necessary data to create

well-performing products. This might reduce the

scope of the patients’ interests that the government regards as legitimate to a subset – the

few ones deserving protection.

Euphemistic portrayal of use cases

Digital technologies can substantially contribute to

advancing the protection of our environment,

resources, climate and biodiversity, as well as clean

up our air, soil and water. (p. 20)

Technologies are here portrayed as a miracle

solution to all our environmental problems,

linguistically enforced by listing many concepts.

Albeit in the following sentence potential negative

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impacts are mentioned, they are soon after said to

be handled simply by promoting research on

socially- and ecologically-oriented AI applications.

Going forward, it will be possible to use AI to

control traffic flows in a way that prevents

congestion and delays. Looking for a parking space

will soon become a thing of the past as AI-based

connected cars learn to identify the nearest

available space. AI-based logistics systems will help

optimize logistics capacity in a way that reduces

the number of unladen transport vehicles on the

move. (p. 35)

This description of technical possibilities mirrors

my results, yet here they are said to

deterministically become reality. We won’t need to look for a parking space anymore because AI

guides us, apparently no matter how many cars or

parking lots there are. Other options (such as fewer

cars overall) are not considered. The reasoning is

blind to any other motif than efficiency, not

acknowledging that e.g. distorted prices might

keep the logistic sector from minimizing unladen

transport.

The potential of AI to serve society as a whole is to

simultaneously increase productivity and the

wellbeing of the workforce: monotonous or

dangerous tasks can be delegated to machines so

that human beings can focus on creative problem

solving. (p. 26)

Productivity is ranked as equally desirable as the

wellbeing of employees. The fact that not everyone

wants to engage in creative problem solving is

ignored. In German, the wording

‘gesamtgesellschaftliches Potenzial’ (literally

entire-societal potential) is not uncommon yet

irritating. This artificial emphasis on how these two

potentials of AI affect all of us as society seems

unjustified when considering who gains from

increasing productivity. In addition, many

monotonous tasks outside the realm of

manufacturing cannot be automated soon, e.g.

cleaning or hairdressing.

Global competition as the highest goal

We want to make Germany and Europe a leading

center for AI and thus help safeguard Germany’s competitiveness in the future. (p. 6)

This is mentioned as the first major goal in the

executive summary, emphasizing the centrality of

competitiveness to the government. It remains

unclear how Germany’s competitiveness relates to those of other European countries – can Germany

and Europe be equally competitive? Usually,

competition implies a hierarchy of the ‘fitness’ of firms, regions or nations.

76

We want to help our companies to make better use

of the potential of AI technologies deriving from

research so that they can thrive in the face of

international competition. The Federal

Government therefore intends to prioritize the

funding of AI applications in the business sector,

and particularly in small and medium-sized

enterprises. (p. 4)

In the pursuit of competitiveness, the government

intends to subsidize the German industry, with

research treated as a service for business. Yet, such

direct interferences and focus on AI technology

and SMEs in particular theoretically goes against

economic liberalist doctrines of neutrality towards

sectors and technology. This illustrates the

multidimensionality or inconsistency of the

government’s discourses, despite not putting into doubt the overarching goal of competitiveness.

This means that automotive companies need to

work together when it comes to the

generation/recording, management and analysis of

driving and sensor data. Companies will only be

able to survive in global competition if they work

together on the implementation of AI systems for

autonomous driving and on ensuring that these are

secure. (p. 35)

Here the government seems willing to risk greater

monopolization in their quest for competitiveness.

Later on in the strategy, they also mention

planning to change the current competition law to

allow for novel ways of sharing data. Apparently,

the only foreseen way to confront the current tech

oligarchy is to form part of it. Instead of

challenging the dynamics of network effects and

‘winner takes all’ markets, they are treated as givens that demand for adaption.