Quadratic Metric Elicitation for Fairness and Beyond

Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi O Koyejo

Keywords: fairness, classification, metrics, evaluation, quadratic, polynomial, supervised learning

TL;DR: The paper develops a strategy for eliciting more flexible multiclass metrics defined by quadratic functions of rates and shows its application in eliciting quadratic violation-based group-fair performance metrics.

Abstract: 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.