Fair Performance Metric Elicitation

• URL: http://arxiv.org/abs/2006.12732v3
• Date: Tue, 3 Nov 2020 06:14:31 GMT
• Title: Fair Performance Metric Elicitation
• Authors: Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo
• Abstract summary: We consider the choice of fairness metrics through the lens of metric elicitation. We propose a novel strategy to elicit group-fair performance metrics for multiclass classification problems.
• Score: 29.785862520452955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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