Responsibility Attribution

Responsibility attribution focuses on determining who or what is accountable for outcomes, particularly in complex systems like those involving AI or multiple interacting agents. Current research emphasizes developing computational frameworks, often employing techniques like Monte Carlo Tree Search and structural causal models within Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs), to efficiently and fairly assign responsibility. These efforts aim to improve accountability in AI systems and enhance safety and transparency in various domains, including workplace safety and healthcare. The ultimate goal is to create more robust and ethically sound systems by clarifying responsibility and facilitating effective intervention.