A0991
Title: Fair conformal prediction
Authors: Linjun Zhang - Rutgers University (United States) [presenting]
Abstract: Multi-calibration is a powerful and evolving concept originating in the field of algorithmic fairness. For a predictor $f$ that estimates the outcome y given covariates $x$, and for a function class $C$, multi-calibration requires that the predictor $f(x)$ and outcome y are indistinguishable under the class of auditors in $C$. Fairness is captured by incorporating demographic subgroups into the class of functions $C$. Recent work has shown that, by enriching the class $C$ to incorporate appropriate propensity re-weighting functions, multi-calibration also yields target-independent learning, wherein a model trained on a source domain performs well on unseen, future target domains(approximately) captured by the re-weightings. The multi-calibration notion is extended, and the power of an enriched class of mappings is explored. HappyMap, a generalization of multi-calibration, is proposed, which yields a wide range of new applications, including a new fairness notion for uncertainty quantification (conformal prediction), a novel technique for conformal prediction under covariate shift, and a different approach to analyzing missing data, while also yielding a unified understanding of several existing seemingly disparate algorithmic fairness notions and target-independent learning approaches. A single HappyMap meta-algorithm is given that captures all these results, together with a sufficiency condition for its success.
