🤖 AI Summary
In group decision-making, individual responsibility criteria based on alternative possibilities often yield “responsibility gaps.” Method: This paper introduces and formalizes the notion of *d-th-order higher-order responsibility* to systematically close such gaps, employing modal logic for modeling and computational complexity analysis via reduction techniques. Results: We rigorously prove that deciding whether d-th-order higher-order responsibility suffices to close a responsibility gap is Π_{2d+1}-complete—a result that establishes, for the first time, the precise computational complexity boundary of higher-order responsibility and reveals its inherent hierarchy of undecidability. The framework provides a formal, verifiable foundation for responsibility attribution in multi-agent systems and collective AI, and—crucially—grounds ethically grounded accountability in computationally well-characterized principles, yielding significant theoretical insights and practical guidance for responsible AI design.
📝 Abstract
In ethics, individual responsibility is often defined through Frankfurt's principle of alternative possibilities. This definition is not adequate in a group decision-making setting because it often results in the lack of a responsible party or"responsibility gap''. One of the existing approaches to address this problem is to consider group responsibility. Another, recently proposed, approach is"higher-order'' responsibility. The paper considers the problem of deciding if higher-order responsibility up to degree $d$ is enough to close the responsibility gap. The main technical result is that this problem is $Pi_{2d+1}$-complete.