48 citations · 109 across the 12 of their papers we have counts for
12 papers
Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems
Mieke Wilms, Christoph Heitz
Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might require sacrificing some performance…
First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz +1
Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off aga…
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…
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
Teresa Scantamburlo, Joachim Baumann, Christoph Heitz
An implicit ambiguity in the field of prediction-based decision-making regards the relation between the concepts of prediction and decision. Much of the literature in the field ten…
Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation
Joachim Baumann, Christoph Heitz
Ensuring fairness of prediction-based decision making is based on statistical group fairness criteria. Which one of these criteria is the morally most appropriate one depends on 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…
Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency
Joachim Baumann, Anikó Hannák, Christoph Heitz
Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizin…
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…