1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CY2022★ 1 cited
Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law
Meike Zehlike, Alex Loosley, Håkan Jonsson +2
Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a 'fair…
cs.CY2020
Towards a Flexible Framework for Algorithmic Fairness
Philip Hacker, Emil Wiedemann, Meike Zehlike
Increasingly, scholars seek to integrate legal and technological insights to combat bias in AI systems. In recent years, many different definitions for ensuring non-discrimination…
cs.CY2017
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
Meike Zehlike, Philipp Hacker, Emil Wiedemann
Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learni…