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Fair Performance Metric Elicitation

Machine Learning 2020-11-04 v3 Machine Learning

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.

Keywords

Cite

@article{arxiv.2006.12732,
  title  = {Fair Performance Metric Elicitation},
  author = {Gaurush Hiranandani and Harikrishna Narasimhan and Oluwasanmi Koyejo},
  journal= {arXiv preprint arXiv:2006.12732},
  year   = {2020}
}

Comments

The paper to appear at NeurIPS 2020. This version includes the camera-ready edits. 31 pages, 6 figures, and 2 tables

R2 v1 2026-06-23T16:32:35.971Z