Moral responsibility is a major concern in automated decision-making, with applications ranging from self-driving cars to kidney exchanges. From the viewpoint of automated systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed tractably, given the split-second decision points faced by the system? By building on deep tractable probabilistic learning, we propose a learning regime for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.
Hammond and Belle REF utilized tractable probabilistic learning to induce models of moral scenarios and blameworthiness automatically from datasets of human decision-making, and computed judgments tractably from the obtained models.
Deep Tractable Probabilistic Models for Moral Responsibility
ArXiv
ArXiv
Computer Science