78 citations · 145 across the 12 of their papers we have counts for
13 papers
The Argument and the Letterhead: Source-Position Coherence in AI Evaluation
Michele Loi
An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive studies and…
"This Is So Claude!" Towards a Theory of the Recognition of AI Character Without Reidentification
Michele Loi
Users sometimes judge that an unfamiliar response is "so Claude." What does this judgment recognize, if it does not identify which model, process, conversation, or mind produced th…
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…