CUED Publications database

An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

Wang, H and Grgic-Hlaca, N and Lahoti, P and Gummadi, KP and Weller, A An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision. (Unpublished)

Full text not available from this repository.


The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropriate similarity metric. In this work, we attempt to learn such similarity metric from human annotated data. We gather a new dataset of human judgments on a criminal recidivism prediction (COMPAS) task. By assuming the human supervision obeys the principle of individual fairness, we leverage prior work on metric learning, evaluate the performance of several metric learning methods on our dataset, and show that the learned metrics outperform the Euclidean and Precision metric under various criteria. 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.

Item Type: Article
Uncontrolled Keywords: cs.CY cs.CY cs.AI cs.LG
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 14 Nov 2019 02:18
Last Modified: 18 Feb 2021 18:14