5 citations · 10 across the 8 of their papers we have counts for
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What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice
Corinna Hertweck, Christoph Heitz, Michele Loi
In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy -- wh…
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
Joachim Baumann, Corinna Hertweck, Michele Loi +1
Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems. However, these metrics are still insufficiently linked to philo…
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs
Corinna Hertweck, Joachim Baumann, Michele Loi +2
In prediction-based decision-making systems, different perspectives can be at odds: The short-term business goals of the decision makers are often in conflict with the decision sub…
People are not coins. Morally distinct types of predictions necessitate different fairness constraints
Eleonora Vigano', Corinna Hertweck, Christoph Heitz +1
A recent paper (Hedden 2021) has argued that most of the group fairness constraints discussed in the machine learning literature are not necessary conditions for the fairness of pr…
A Systematic Approach to Group Fairness in Automated Decision Making
Corinna Hertweck, Christoph Heitz
While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in prac…
On the Moral Justification of Statistical Parity
Corinna Hertweck, Christoph Heitz, Michele Loi
A crucial but often neglected aspect of algorithmic fairness is the question of how we justify enforcing a certain fairness metric from a moral perspective. When fairness metrics a…