Search Results for author:
Found 2 papers, 1 papers with code
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
