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Found 6 papers, 3 papers with code
Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics
Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems.
A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs
This allows us to derive a fairness score that we then compare to the decision maker's utility.
Gradual (In)Compatibility of Fairness Criteria
This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.
A Systematic Approach to Group Fairness in Automated Decision Making
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 practice.
On the Moral Justification of Statistical Parity
This means that the question of whether independence should be used or not cannot be satisfactorily answered by only considering the justness of differences in the predictive features.
Towards Data-Driven Affirmative Action Policies under Uncertainty
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs.
