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Title: Quadratic metric elicitation for fairness and beyond
Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context. However, available elicitation strategies have been limited to linear (or quasi-linear) functions of predictive rates, which can be practically restrictive for many applications including fairness. This paper develops a strategy for eliciting more flexible multiclass metrics defined by quadratic functions of rates, designed to reflect human preferences better. We show its application in eliciting quadratic violation-based group-fair metrics. Our strategy requires only relative preference feedback, is robust to noise, and achieves near-optimal query complexity. We further extend this strategy to eliciting polynomial metrics – thus broadening the use cases for metric elicitation.  more » « less
Award ID(s):
2046795 1909577
Author(s) / Creator(s):
; ; ;
Cussens, James; Zhang, Kun
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
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