How to Choose a Fairness Measure: A Decision-Making Workflow for Auditors

Federica Picogna, Jacques de Swart, Heysem Kaya and Ruud Wetzels
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Federica Picogna: Nyenrode Business University
Heysem Kaya: Utrecht University

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Abstract: Recent developments in Artificial Intelligence (AI) have greatly benefited society, but they also come with risks. One of those risks is that AI has the potential to discriminate against certain groups of people. To address that risk, benchmark regulations such as the AI Act have been cre- ated, requiring AI systems to be fair and tasking auditors with ensuring their compliance. In order to do so, auditors use fairness measures. However, selecting a specific definition of fairness from the various available options and choosing a fairness measure from the numerous possibilities com- plicates the auditing process, making it challenging for auditors to correctly assess AI fairness. To assist them, we created a decision-making workflow that guides the auditor through the selection process of the most appropriate measure and, consequently, the most suitable definition of fairness. To simplify the use of this workflow, we have integrated it into the open-source program JASP for Audit and demonstrated its functionality with two examples: the COMPAS recidivism and the DUO case.

Date: 2025-03-26
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:cpxmf_v2

DOI: 10.31219/osf.io/cpxmf_v2

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