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A Critical Survey on Fairness Benefits of Explainable AI

Title data

Deck, Luca ; Schoeffer, Jakob ; De-Arteaga, Maria ; Kühl, Niklas:
A Critical Survey on Fairness Benefits of Explainable AI.
In: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT). - Rio de Janeiro, Brazil , 2024
DOI: https://doi.org/10.1145/3630106.3658990

Official URL: Volltext

Abstract in another language

In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two concepts. Based on a systematic literature review and a subsequent qualitative content analysis, we identify seven archetypal claims from 175 scientific articles on the alleged fairness benefits of XAI. We present crucial caveats with respect to these claims and provide an entry point for future discussions around the potentials and limitations of XAI for specific fairness desiderata. Importantly, we notice that claims are often $(i)$ vague and simplistic, $(ii)$ lacking normative grounding, or $(iii)$ poorly aligned with the actual capabilities of XAI. We suggest to conceive XAI not as an ethical panacea but as one of many tools to approach the multidimensional, sociotechnical challenge of algorithmic fairness. Moreover, when making a claim about XAI and fairness, we emphasize the need to be more specific about what kind of XAI method is used, which fairness desideratum it refers to, how exactly it enables fairness, and who is the stakeholder that benefits from XAI.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Explainable AI; Algorithmic Fairness; Critical Survey
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Informatics and Human-Centered Artificial Intelligence > Chair Business Informatics and Human-Centered Artificial Intelligence - Univ.-Prof. Dr.-Ing. Niklas Kühl
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 31 May 2024 06:09
Last Modified: 31 May 2024 06:09
URI: https://eref.uni-bayreuth.de/id/eprint/89620