Learning Optimal Individualized Decision Rules with Conditional Demographic Parity
Individualized decision rules (IDRs) have become increasingly prevalent in societal applications such as personalized marketing, healthcare, and public policy design. However, a critical ethical concern arises from the potential discriminatory effects of IDRs trained on biased data. These algorithms may disproportionately harm individuals from minority subgroups defined by sensitive attributes like gender, race, or language. To address this issue, we propose a novel framework that incorporates demographic parity (DP) and conditional demographic parity (CDP) constraints into the estimation of optimal IDRs. We show that the theoretically optimal IDRs under DP and CDP constraints can be obta...
| Authors | Wenhai Cui, Wen Su, Donglin Zeng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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