Causal humility guides fair AI under uncertain cause effects
Causal Humility: Governing the Authority of Fairness Claims Under Causal Uncertainty
Computers and SocietyArtificial Intelligence
Summary
AI systems make important decisions that need to be fair, but understanding why unfair outcomes happen is tricky because it involves complicated causes that are hard to pin down. The authors introduce "causal humility," a way to show where assumptions and uncertainties lie when deciding if AI is fair. They propose a tool called Causal Cards to help document and share these uncertainties clearly. This helps make fairness claims more honest and keeps decisions open to challenge when the facts are not certain.
What this means in practice
- •For ai fairness auditors: Use Causal Cards to transparently record assumptions and uncertainties in AI fairness evaluations for better governance decisions.
- •For public policy teams: Adopt causal humility principles to ensure fairness interventions acknowledge causal uncertainty and maintain accountability in policy decisions.
Tested on one dataset.
Authors
Khadija Zanna, Rodrigo Ferreira, Frederick L. Oswald, Akane Sano
Abstract
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 representational choices. We introduce causal humility as a governance framework for managing the relationship between epistemic uncertainty and causal authority. Drawing on causal fairness research, epistemology, political philosophy, and science and technology studies, causal humility has four requirements: making consequential assumptions and omissions visible; preserving contestability across defensible causal accounts; recognizing indeterminacy as a legitimate analytical outcome; and maintaining proportionality between evidentiary support and institutional authority or action. We operationalize these requirements through Causal Cards, a documentation artifact for recording the causal basis, assumptions, omissions, model dependence, and governance implications of fairness claims. Using the UCI Adult dataset as a worked epistemic demonstration, we show how different causal discovery procedures applied to the same observational data render fairness-relevant pathways visible, absent, or unresolved. Causal humility treats such disagreement as information about the limits of what can responsibly be claimed and the authority those claims should carry.