Causal Humility: Governing the Authority of Fairness Claims Under Causal Uncertainty
AI systems increasingly shape consequential decisions in employment, lending, healthcare, and public services, making fairness an important problem for auditing and governance. Fairness assessment often requires more than identifying disparities in outcomes; it also requires understanding how those disparities arise. Causal approaches to algorithmic fairness address this explanatory problem by representing pathways through which protected attributes and other factors may influence outcomes. Yet causal reasoning in sociotechnical systems remains uncertain: relevant confounders may be unobserved, causal directions contested, and conclusions sensitive to modeling assumptions and representati...
| Authors | Khadija Zanna, Rodrigo Ferreira, Frederick L. Oswald et al. |
|---|---|
| Primary category | cs.CY |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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