new global lead –ethical ai and governance massimo pellegrino · 2019. 12. 3. · technology...

Responsible Artificial Intelligence

Global Lead – Ethical AI and Governance

Massimo Pellegrino

Why doeasartificialintelligenceneed ethics?

Moral permissibilityof the goals

SocietalControl


TerrorismMoral permissibility

of the goals

Chemicaland biological warfare


Criminal operations


The (unintentional)consequences of algorithms

The ValueAlignment problem

The (unintentional) consequences of algorithms

The inscrutability of the machine learning models


Omega Bank in Milan is about to launch a Digital Lending solution to offer short term, pre-approved small loans to its existing customer base through mobile devices

Customers will find about the loan via social media, their internet banking application, and SMS. In minutes, the customer will gain access to the Digital Lending app and is able to apply with minimal documentation.

Let's try an example… How does it work


• Lawfulness and compliance• Data Privacy

• Fairness• Security

• Adversarial attacks• API Theft

• Human Agency• Accountability• Interpretability

• Explainability

The Digital Lending solution can carries the following ethical risks:


Trust

European legislation – GDPR framework

GDPR Provision

Specifically related to profiling

Provide information

• Article 13• Article 14• Whereas 60

Data controllers must inform data subjects also of the existence of and logic involved in any automated decision-making process,including profiling

Grant access• Article 15• Whereas 63

Data controllers must provide data subjects with information regarding the existence of any automated decision-making process and the related logic involved concerning him/her

Grant objection

• Article 21• Whereas 70

Data controllers must grant data subjects the right to object if the processing, including profiling, is based on public interest or on the legitimate interest of the data controller itself

No solely automated decision

• Article 22• Whereas 71 Data controllers must not take decision based only on automated means

Perform DPIA

• Article 35• Whereas 75

Data controller must perform a DPIA where a type of processing in particular using new technologies, and taking into account the nature, scope, context and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons

General provisions

Apply principles Article 5 Data controllers must process personal data, also for profiling purposes, in compliance with the lawfulness, fairness, transparency,

purpose limitation, data minimisation, accuracy, storage limitation, integrity, confidentiality and accountability principles

Ask consent Article 6The processing of personal data, also for profiling purposes, is lawful only if one of the following applies: data subject’s consent, it is necessary for the performance of a contract, for compliance with a legal obligation, to protect the vital interests of the data subject, for public interests or for the data controller’s legitimate interest.

Lawfullness and Compliance concerning Data Privacy are becoming more complex

Fairness, is a social construct, a value-based choice. There are more than 30 mathematical

definitions of fairness, and when we choose one definition of fairness, we violate some aspect of

fairness captured by the other definitions. In other words, it is impossible for every decision

to be fair to all parties.


Fairness DefinitionWhat are the different fairness definitions? Which one should we used for what purpose?

Statistical Parity: people from both protected (female) and unprotected (male) groups should have equal probability of getting loans approved. Mathematically, it implies equal acceptance rates across groups.

Accepted = 8Total males = 16Acceptance Rate = 8/16= 50%

Accepted = 4Total females = 8Acceptance Rate =4/8 = 50%

Loan

Males Females

Loan



Equal Opportunity: people who pay back their loans, should have an equal opportunity of getting the loan in the first place. Mathematically, it implies equal True Positive Ratesacross various groups.

True Positives = 6False Negatives = 2True Positive Rate = 6/8= 75%

True Positives = 3False Negatives = 1True Positive Rate =3/4 = 75%

Loan

Males Females

Loan

Default Non-Default



Overall Accuracy: people who pay back their loans should get loans, and people who default, should not get loans. Mathematically, it implies equal overall accuracy across various groups.

True Positives = 6False Positives =2True Negatives =6False Negatives = 2

Overall Accuracy=TP+TN/Total = 12/16 = 75%

True Positives = 3False Positives =1True Negatives =3False Negatives = 1

Overall Accuracy=TP+TN/Total = 6/8 = 75%

Loan

Males Females

Loan

Default Non-Default


DatasetFairness DefinitionThe fairness definition that we agree to apply to the decision• Accuracy• Statistical parity• Equal Opportunity

Bias is estimated relative to a specific dataset• German Credit dataset• FICO dataset

Protected AttributesThe variables on which we do not want to discriminate• Gender (male/female)• Marital status

(single/married/divorced)

Bias detection is based on the decision being made, the protected attribute, fairness definition chosen and the dataset provided

DecisionThe target variable or decision under consideration

• Loan approval

• Property ownership

Security

AdversarialAttack

Hackers could trick the Digital Lending algorithm to force it to grant loans to people

with specific characteristics

APITheft

Hackers could get access to the chatbot API and provide deceitful information to the Bank’s customers


HumanAgencyShould the Digital Lending application make completely autonomous decisions?


AccountabilityIn case of malfunctioning, who is accountable for what?


InterpretabilityData controllers must inform data subjects also of the existence of and logic involved in any automated decision-making process, including profiling

• GDPRS• Article 13• Article 14

• Whereas 60


With the Digital Lending system Omega Bank wants to pursue the goals of lending money to the highest number possible of clients maximizing at the same time the probability of loan repayment.

In doing so, a primary objective of the bank is to treat its clients with fairness, especially with regard to gender discrimination.To achieve this goals, a Statistical parity algorithm is used, which means that males and females have equal probability of getting loans approved (for more information on how the system works visit http://xxxxxxxxxx)

Interpretability example


The Digital Lending system is GDPR compliant and the current version has been tested with a xx% accuracy (for more information on how the system was developed and how it is maintained visit http://xxxxxxxxxx)

http://xxxxxxxxxx/

http://xxxxxxxxxx/

What can companiesand organizations do to mitigateethical AI risks?

1. Define an ethicalAI Code of Conduct

Apply Ethics & Legal throughout, helping clients understand the ethical implications of their use of AI

Ethical Approach

Fundamental Human Rights

Epistemic & General

Principles of AI Ethics

Contextualisation

Methodology

Operationalise Ethics through Computational

Ethics

AI Ethics Framework

PwC’s Ethical AI FrameworkEpistemic and General Ethical AI Principles

• Interpretability

• Reliability, Robustness and Security

Epistemic Principles

Set of principles that are objective and do not prescribe any specific behaviour consistent with a specific rule or value. Rather, they represent the indispensable conditions of knowledge without which it is impossible to determine whether an AIS is consistent with an ethical principle.

• Beneficial AI

• Fairness

• Human Agency

• Safety

• Data Privacy

• Lawfulness & Compliance

• Accountability

General Principles

Set of behavioural principles that have a universal scope. These principles, when contextualised, must stand valid in any possible cultural and geographical application.

2. Define an ethicalAI Governance Framework

Ensure end-to-end Governance

Model Level

1. S

trateg

y

3. Ecosystem

5. Deployment

6. Operate and Monitor

Corporate Strategy

Industry Standards & Regulations

Internal Policies & Practices

Operational Support

Compliance

Portfolio Management

Program Oversight

Delivery Approach

Technology Roadmap

Sourcing

2. P

lan

nin

g

Transition & Execution

Ongoing monitoring

Data Extraction

4. D

evel

op

men

t

Change Management

Evaluation & Check-in

Model Integration

Solution Design

Business & Data Understanding

Pre-Processing

Model Building

Who Benefits:• C-Suite• Process Owners• Risk & Compliance• Data Scientists• Consumers• Regulators

3. Identify the software tools necessary for an effective and consistent implementation and monitoring of the ethical AI principles

• Ethical AI Governance Platform• Bias detection and avoidance

applications• Explainability applications• Applications to manage data

privacy preferences

Someexamples

4. Communicate with external and internal stakeholders to find operational solutions to ethical tensions and trade-offs

"So the rate of improvement is really dramatic. We have to figure out some way to ensure that the advent of

digital super intelligence is one which is symbiotic with humanity. I think that is the single biggest existential

crisis that we face and the most pressing one."Elon Musk – CEO of Tesla, SpaceX, and OpenAI

Thank you!

new global lead –ethical ai and governance massimo pellegrino · 2019. 12. 3. · technology...

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