AIJul 3, 2025

Responsibility Gap and Diffusion in Sequential Decision-Making Mechanisms

arXiv:2507.02582v11 citationsh-index: 5
Originality Highly original
AI Analysis

This addresses foundational issues in AI ethics and law by providing complexity results for responsibility attribution in multi-agent systems, though it is incremental in its theoretical analysis.

The paper tackles the computational complexity of responsibility diffusion and gap in collective decision-making mechanisms, showing that diffusion-free mechanisms are Π₂-complete, gap-free mechanisms are Π₃-complete, and their intersection is Π₂-complete.

Responsibility has long been a subject of study in law and philosophy. More recently, it became a focus of AI literature. The article investigates the computational complexity of two important properties of responsibility in collective decision-making: diffusion and gap. It shows that the sets of diffusion-free and gap-free decision-making mechanisms are $Π_2$-complete and $Π_3$-complete, respectively. At the same time, the intersection of these classes is $Π_2$-complete.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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