@InProceedings{hiranandani_489,
    title = {Quadratic metric elicitation for fairness and beyond},
    author = {Hiranandani, Gaurush and Mathur, Jatin and Narasimhan, Harikrishna and Koyejo, Oluwasanmi},
    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.}
}

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