2020/11/03 by Gaurush Hiranandani, Jatin Mathur, Hiranandani, Gaurush +5
Social Sciences · Neuroscience · Decision Sciences · #Experimental Behavioral Economics Studies #Free Will and Agency #Decision-Making and Behavioral Economics
paper · pdf · doi:10.48550/arxiv.2011.01516
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.