mlsanityData to 14 Oct 2026

Causal Responsibility Attribution for Human-AI Collaboration

arXiv 2411.03275 ↗First posted 5 Nov 2024cs.AI cs.HC stat.APcited by 2

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Abstract (arXiv)

As Artificial Intelligence (AI) systems increasingly influence decision-making across various fields, the need to attribute responsibility for undesirable outcomes has become essential, though complicated by the complex interplay between humans and AI. Existing attribution methods based on actual causality and Shapley values tend to disproportionately blame agents who contribute more to an outcome and rely on real-world measures of blameworthiness that may misalign with responsible AI standards. This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems, measuring overall blameworthiness while employing counterfactual reasoning to account for agents' expected epistemic levels. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 5 Nov 2024.

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13 of 35 references.

References · 13

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  8. 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
  9. 2018Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibilitycited by 32
  10. 2024Evaluating Human-AI Collaboration: A Review and Methodological Frameworkcited by 27
  11. 2021Arbitrage-free neural-SDE market modelscited by 19
  12. 2019Blameworthiness in Multi-Agent Settingscited by 11
1 more
  1. 2022Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processescited by 8

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Causal Abstraction Learning based on the Semantic Embedding Principlecited by 9
  2. 2025Who Gets Credit or Blame? Attributing Accountability in Modern AI Systemscited by 2