Fair regression under localized demographic parity constraints
Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full distributional DP is often overly restrictive in regression and can lead to substantial accuracy loss. We propose a relaxation of DP tailored to regression, enforcing parity only at a finite set of quantile levels and/or score thresholds. Concretely, we introduce a novel (${\ell}$, Z)-fair predictor, which imposes groupwise CDF constraints of the form F f |S=s (z m ) = ${\ell}$ m for prescribed pairs (${\ell}$ m , z m ). For this setting, we derive closed-form characterizations of the optimal fair discretized pr...
| Authors | Arthur Charpentier, Christophe Denis, Romuald Elie et al. |
|---|---|
| Primary category | stat.ML |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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