Moral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such 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 effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework 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.

道德责任是自动驾驶系统中的主要问题，其应用范围包括无人驾驶汽车到肾脏交换。尽管最近有尝试将责任感和责备形式化，但在类似概念中，这些形式主义中的学习问题尚未得到解决。从此类系统的角度来看，紧迫的问题是：（a）如何从数据中自动提取和学习道德情景和责任感模型？（b）考虑到某些系统面临的瞬间决策点，如何有效地，有效地计算判断？通过建立受约束的易处理概率学习，我们提出并实现了一种混合的（数据驱动的和基于规则的方法之间）学习框架，用于从数据自动诱导此类情景模型并从中进行合理推理。