用因果模型公平划分人机协作中的责任归属
Causal Responsibility Attribution for Human-AI Collaboration
- 基于结构因果模型，结合反事实推理分析责任
- 考虑人类认知水平差异，避免过度归责单一主体
- 适用于医疗、自动驾驶等高风险人机协作场景
随着人工智能系统在各领域决策中扮演越来越重要的角色，对不良结果的责任归属变得至关重要，但复杂的人员与AI交互使问题愈发复杂。现有基于实际因果和Shapley值的归因方法往往过度归咎于对结果贡献较大的一方，并依赖现实中的可责性度量，可能与负责任AI的标准不一致。本文提出一种基于结构因果模型（SCMs）的因果框架，系统性地衡量人机系统中的责任，综合反事实推理以考虑各参与方预期的认知水平。两个案例研究展示了该框架在多样化人机协作场景中的适应性。
原文摘要 · Abstract (English)
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.
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