Fair Performance Metric Elicitation
Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo
摘要
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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引用它的顶会 Paper5
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它引用的顶会 Paper2
- Robust Optimization for Fairness with Noisy Protected GroupsSerena Lutong Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter 等NeurIPS 2020 · 被引用 134 次
- Consistent Plug-in Classifiers for Complex Objectives and ConstraintsShiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna NarasimhanNeurIPS 2020 · 被引用 8 次
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