78 citations · 145 across the 12 of their papers we have counts for
5 papers · 1 filter
Is calibration a fairness requirement? An argument from the point of view of moral philosophy and decision theory
Michele Loi, Christoph Heitz
In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of fals…
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…
Group Fairness Is Not Derivable From Justice: a Mathematical Proof
Nicolò Cangiotti, Michele Loi
We argue that an imperfect criminal law procedure cannot be group-fair, if 'group fairness' involves ensuring the same chances of acquittal or convictions to all innocent defendant…