Towards Engineering AI Software for Fairness

A framework to help design fair, accountable and transparent algorithmic decision-making systems

Master thesis (2020)

Authors

C.B. Lazo Electrical Engineering, Mathematics and Computer Science

Contributors

G.J.P.M. Houben Web Information Systems - EEMCS (supervisor 2)
C. Lofi Web Information Systems - EEMCS (supervisor 1)
Ranga Rao Venkatesha Prasad Embedded Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Claudio Lazo
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Published Date

25-08-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Algorithmic decision-making (ADM) is becoming increasingly prevalent in society, due to the rapid technological developments in Artificial Intelligence. ADM make substantially impactful decisions about people: diagnosing whether we have a disease, what news and which ads we get to see, whether we
are eligible for a job, benefits, a college or a loan, they show us personalized media and news, and steer the car that drives us home. However, ADM brings about ethical, legal and social issues by inheriting and perpetuating human biases, learning to discriminate—even learning gender or racial stereotypes, and lacking transparency and accountability. This unexpected and biased behaviour arises because these software systems are usually built without the specification of fairness requirements (i.e. what fair behaviour is expected of the system). We envision a Software Engineering for Values (SEfV) method that solves this problem.
This study addresses that specification problem, aiming to help practitioners design ADM software for fairness. Using literature in social sciences—specifically organizational justice—the human value of fairness has been conceptualized in regard to ADM. This resulted in a fairness tree with four dimensions (procedural, distributive, informational and interpersonal fairness), which is further specified into 31 fairness norms. Subsequently, the fairness tree is related to current measures of fairness and techniques. Finally, we put forward the Software Engineering for Values (SEfV) framework, based on the principles of Software Engineering and Design for Values, and show how it can be applied to design ADM for fairness.
Experiments were conducted where participants (N = 12) performed a design task (M = 3, 75 requirements specified) and an audit task for a hypothetical loan decision system—using a prototype of the SEfV framework. Participants found the prototype useful for both design as auditing, especially
as a tool for reflecting on fairness considerations. This suggests that a high fidelity version would be useful for practitioners.