Fairness under uncertainty in sequential decisions
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural inequalities, but they can support socio-technical decision systems by surfacing discriminatory biases, clarifying trade-offs, and enabling governance. Although fairness is well studied in supervised learning, many real ML applications are online and sequential, with prior decisions informing future ones. Each decision is taken under uncertainty due to unobserved counterfactuals and finite samples, with dire consequences for under-represented groups, systematically under-observed due to historical exclusion and ...
| Authors | Michelle Seng Ah Lee, Kirtan Padh, David Watson et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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