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Data Ethics

N8 PRP CPD Module 7

Ethics and Data Governance

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“…police may be the primary actors in the

system to solve crime, but they do not have

to be the primary actors in the system to

reduce risk…” “…the insight that

predictive policing data can identify the

problem does not indicate the choice of

remedy…”

“…the bigger question involves whether a

police-focused remedy helps or harms the

larger good…”

Guthrie Ferguson (2017): 169

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Ethics

• Traditionally about individual moral agency and responsibility, based on assumptions of individuality and free will

• Degree to which an entity posses moral agency determines the responsibility of that entity:

– Causality

– Knowledge

– Choice

• Assumptions of individuality and free will both challenged by big data and its impact on an individual’s ability to understand its potential to make informed decisions…

• Big data – philosophical problem of many hands i.e. the effect of many actors contributing to an action distributed morality

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Research ethics

Research ethics can be thought of as the ethics of planning, conducting

and reporting research.

Research ethics are fundamental:

• to the production of high quality research;

• to the protection of research participants and researchers;

• and for the production of research which is in the public good.

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Core principles of research ethics

• Minimise the risk of harm (to participants and to yourself

as researcher)

• Obtain informed consent

• Protect anonymity and/or ensure confidentiality

• Provide the right to withdraw without question (up to a specific

point in the process)

• Avoid deceptive practices

• Analysis, Dissemination and Impact

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Data ethics

A new branch of ethics – study and evaluation of moral problems related to data, algorithms and corresponding practices to formulate and support morally good solutions

Ethical DILEMMA “… a situation in which two principles are in conflict with each other. Unlike problems, which have solutions (even if we are unable to grasp them), dilemmas do not have solutions. They are choices we have to make in the face of conflicting principles…” Rowland, S. (2000): 8.

• Emphasises complexity of ethical challenges posed by data science

• Requires a macro-ethics i.e. an overall framework, not narrow or ad hoc approaches

• Aims to enable the maximum value from data science for society

Floridi and Taddeo (2016)

The increase of data, the increasing use of data, and the growing reliance on algorithms has huge potential for solving social problems, but also brings significant ethical dilemmas

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What is big data?

Technological

• Data that exceeds the processing capacity of conventional datasets

• “3-V” high volume, high velocity, high variety information assets that demand cost effective, innovative forms of processing for enhanced decision-making and insight

Social

• Things one can do at a

large scale that cannot be

done at a smaller scale,

to extract new insights or

create new forms of value

in ways that change

relationships

• Production of increased

institutional awareness

and power

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Big data predictions will (are) have significant influence on human activity and human decision-making radically change society (pre and post big data societies)…“In building a new digital society the values we include and exclude in the new digital structures will define us…” Richards and King (2014): 395

Decisions about big data are political decisions with implications for the distribution of power and in/justice… e.g. collection of data for future, as yet, unplanned or unanticipated use; data brokers; etc

Data ethics need to figure out how to prevent the abuse of big data as a new found source of information and power in a datafied world

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Four qualities of big data

Trend towards an impersonal ethics based on consequences for others…

1. More data than ever before in the history of data

i. Absence of knowledge about what is collected and what it can be used for

2. Big data is organic and messy (format inconsistencies, measurement artefacts) but represents reality more precisely than other forms of data

3. Big data is potentially global, with global reach

4. Big data generates correlations (rather than causations), but suggests causations where there may be none

i. Become more vulnerable to having to believe what we see without knowing the underlying why

ii. Big data make random connections (patterns, correlations) more likely

iii. Ability to discover hidden correlations increases the ability to create ‘incentives’ for less transparent purposes

Zwitter (2014)

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Data ethics: Three intersecting axes

• DATA

– Generation

– Recording

– Curating

– Processing

– Disseminating

– Sharing

– Use

• ALGORITHMS

– AI

– Artificial agents

– Machine learning

– Robots

• CORRESPONDING PRACTICES

– Hacking

– Responsible innovation

– Programming

– Professional codes

Covers all forms of data and the interactions between:• Hardware• Software• Data

Focus is not on specific technologies, but on what digital technologies manipulate

Floridi and Taddeo (2016)

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Three intersecting axes: Data,

Algorithms, Practices

• Ethics of data: ethical problems posed by collecting and analysing data (especially large-scale) and on issues such as using big data in social science, profiling, advertising, open data/science

– Especially identification of individuals or groups through data mining, linkage where this could lead to discrimination / injustice

• Ethics of algorithms: increasingly complex, especially machine learning

– Ethical design and auditing of algorithms to predict and prevent unintended consequences

• Ethics of practices: responsibilities and liabilities of individuals and organisations in charge of data processes, policies and strategies

– Design of professional codes for innovation, development and use progress and protection (consent, user privacy and secondary use)

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Principles for an ethics of big data

1. Privacy as information rules: rules to govern the use of personal

data (privacy is not dead!)

2. Shared private information can remain confidential – consent to

share does not negate privacy

3. Transparency – to help prevent abuses of power and to empower

individuals to share more

4. Identity – big data can compromise identity (ability to define who

you are yourself) as predictions and inferences identify, categorise,

modulate and even determine who we are / might become

regulation of predictions and inferences which are corrosive,

threatening, unjust

Richards and King (2014)

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Policing and (big) data: Context for Data

Ethics

Predictive policing as more than only a policing tool: BRIGHT data

Guthrie Ferguson (2017) five foundational questions:

1. Can you identify the risks that your big data technology is trying to

address?

2. Can you defend the inputs into the system (e.g. sex, race, age,

criminal history, sentence length) (accuracy of data, soundness of

method etc)?

3. Can you defend the outputs of the system – how will they impact on

policing practice and community relations?

4. Can you test the tech (accountability and transparency)?

5. Is police use of technology respectful of the autonomy of the people

it will impact?

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Context: What is the point… of

policing?

• Crime reduction

• Solving crime

• Protecting society

• Upholding the law

• Protecting the vulnerable (preventing situations of vulnerability)

• Arresting perpetrators

• CRIME

• RISK

• NEED

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References

Floridi and Taddeo (2016) ‘What is data ethics?’ Philosophical

Transactions A 374.

Guthrie Ferguson (2017) The Rise of Big Data Policing. New York, New

York University Press.

Richards and King (2014) ‘Big data ethics’ Wake Forest Law Review

49: 393.

Zwitter (2014) ‘Big data ethics’ Big Data and Society July-Dec: 1.