Causal Humility: Governing the Authority of Fairness Claims Under Causal Uncertainty

Key points

• AI systems increasingly shape consequential decisions in employment, lending, healthcare, and public services, making fairness an important problem for auditing and governance.1
• Causal approaches to algorithmic fairness address this explanatory problem by representing pathways through which protected attributes and other factors may influence outcomes.1
• 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.1
• 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.1

Sources (1)

• [1]Causal Humility: Governing the Authority of Fairness Claims Under Causal Uncertainty
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:54 AM

  We introduce causal humility as a governance framework for managing the relationship between epistemic uncertainty and causal authority.

  AI systems increasingly shape consequential decisions in employment, lending, healthcare, and public services, making fairness an important problem for auditing and governance.

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