485 citations · 628 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
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…
FA*IR: A Fair Top-k Ranking Algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo +3
In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility…