There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a continuous stream of new concep- tual frameworks, methods, and tools. How much can we trust them? How much do they actually impact society? We take a critical focus on fair-AI and survey issues, simplifications, and mistakes that researchers and practitioners often under- estimate, which in turn can undermine the trust on fair-AI and limit its contribution to society. In particular, we discuss the hyper-focus on fairness metrics and on optimizing their average performances. We instantiate this observation by dis- cussing the Yule’s effect of fair-AI tools: being fair on average does not imply being fair in contexts that matter. We conclude that the use of fair-AI methods should be complemented with the design, development, and verification practices that are commonly summarized under the umbrella of trustworthy AI

Can We Trust Fair-AI?

Pugnana, Andrea;
2023

Abstract

There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a continuous stream of new concep- tual frameworks, methods, and tools. How much can we trust them? How much do they actually impact society? We take a critical focus on fair-AI and survey issues, simplifications, and mistakes that researchers and practitioners often under- estimate, which in turn can undermine the trust on fair-AI and limit its contribution to society. In particular, we discuss the hyper-focus on fairness metrics and on optimizing their average performances. We instantiate this observation by dis- cussing the Yule’s effect of fair-AI tools: being fair on average does not imply being fair in contexts that matter. We conclude that the use of fair-AI methods should be complemented with the design, development, and verification practices that are commonly summarized under the umbrella of trustworthy AI
2023
Settore INF/01 - Informatica
37th AAAI Conference on Artificial Intelligence
Washington DC, USA
February 7–14, 2023
PROCEEDINGS OF THE 37th AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
AAAI Press
978-1-57735-880-0
Fair Machine Learning; Fairness Metrics; Yule's Effect
   Artificial Intelligence without Bias
   NoBIAS
   European Commission
   Horizon 2020 Framework Programme
   860630
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/136444
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