Fairness Without Reflection? An Empirical Analysis of AI Ethics Tools in the Age of GenAI

Lisa Koeritz, Sonja Pfisterer, Simon David Hirsbrunner
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:101-106, 2026.

Abstract

AI ethics tools designed to operationalise fairness are increasingly insufficient for the challenges posed by generative AI (GenAI). Based on a content analysis of 30 digital AI ethics tools, we find that tools function primarily as compliance artifacts rather than as epistemic devices supporting ethical judgment under change. Technical tools frame fairness as a computational problem inapplicable to GenAI, while governance-oriented tools reduce it to documentation and risk management. We argue that fairness in GenAI must be governed, not computed. This requires a shift toward tools that are adaptable to changing technical landscapes, clarify responsibilities across stakeholders, and support collaborative ethical reflection rather than static verification. This approach better serves the EU AI Act’s requirements for human oversight than simple checklists, and ensures that fairness remains a continuous governance practice.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-koeritz26a, title = {Fairness Without Reflection? An Empirical Analysis of AI Ethics Tools in the Age of GenAI}, author = {Koeritz, Lisa and Pfisterer, Sonja and Hirsbrunner, Simon David}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {101--106}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/koeritz26a/koeritz26a.pdf}, url = {https://proceedings.mlr.press/v350/koeritz26a.html}, abstract = {AI ethics tools designed to operationalise fairness are increasingly insufficient for the challenges posed by generative AI (GenAI). Based on a content analysis of 30 digital AI ethics tools, we find that tools function primarily as compliance artifacts rather than as epistemic devices supporting ethical judgment under change. Technical tools frame fairness as a computational problem inapplicable to GenAI, while governance-oriented tools reduce it to documentation and risk management. We argue that fairness in GenAI must be governed, not computed. This requires a shift toward tools that are adaptable to changing technical landscapes, clarify responsibilities across stakeholders, and support collaborative ethical reflection rather than static verification. This approach better serves the EU AI Act’s requirements for human oversight than simple checklists, and ensures that fairness remains a continuous governance practice.} }
Endnote
%0 Conference Paper %T Fairness Without Reflection? An Empirical Analysis of AI Ethics Tools in the Age of GenAI %A Lisa Koeritz %A Sonja Pfisterer %A Simon David Hirsbrunner %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-koeritz26a %I PMLR %P 101--106 %U https://proceedings.mlr.press/v350/koeritz26a.html %V 350 %X AI ethics tools designed to operationalise fairness are increasingly insufficient for the challenges posed by generative AI (GenAI). Based on a content analysis of 30 digital AI ethics tools, we find that tools function primarily as compliance artifacts rather than as epistemic devices supporting ethical judgment under change. Technical tools frame fairness as a computational problem inapplicable to GenAI, while governance-oriented tools reduce it to documentation and risk management. We argue that fairness in GenAI must be governed, not computed. This requires a shift toward tools that are adaptable to changing technical landscapes, clarify responsibilities across stakeholders, and support collaborative ethical reflection rather than static verification. This approach better serves the EU AI Act’s requirements for human oversight than simple checklists, and ensures that fairness remains a continuous governance practice.
APA
Koeritz, L., Pfisterer, S. & Hirsbrunner, S.D.. (2026). Fairness Without Reflection? An Empirical Analysis of AI Ethics Tools in the Age of GenAI. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:101-106 Available from https://proceedings.mlr.press/v350/koeritz26a.html.

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