Causal Responsibility Attribution for Human-AI Collaboration

Current responsibility attribution methods are inadequate for AI systems: Existing approaches based on actual causality and Shapley values "disproportionately blame agents who contribute more to an outcome," which can unfairly penalize either humans or AI systems depending on their level of involvement, rather than fairly assessing who should be held accountable.

Responsibility requires understanding what agents should have known: The framework accounts for "agents' expected epistemic levels" through counterfactual reasoning, meaning responsibility attribution should consider what information and expertise each party (human or AI) reasonably possessed at the time, not just their technical contribution to the outcome.

One-size-fits-all blame assignment doesn't work: The paper demonstrates through case studies that responsibility attribution must be "adaptable in diverse human-AI collaboration scenarios," suggesting that different contexts—such as medical diagnosis versus content moderation—may require different approaches to fairly assign accountability.

We need formal tools to navigate human-AI accountability: The paper proposes using Structural Causal Models as a systematic framework, suggesting that responsible AI governance requires rigorous mathematical and logical methods rather than informal judgment to determine who bears responsibility for harmful outcomes.

Responsible AI standards differ from real-world legal standards: The framework acknowledges that measures of blameworthiness "may misalign with responsible AI standards," indicating that holding AI systems accountable requires new ethical principles beyond traditional legal liability frameworks.