  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.
