Fair Performance Metric Elicitation

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Authors

Gaurush Hiranandani, Harikrishna Narasimhan, Sanmi Koyejo

Abstract

What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics that best reflect implicit preferences. The use of metric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context, and population at hand. Specifically, we propose a novel strategy to elicit group-fair performance metrics for multiclass classification problems with multiple sensitive groups that also includes selecting the trade-off between predictive performance and fairness violation. The proposed elicitation strategy requires only relative preference feedback and is robust to both finite sample and feedback noise.