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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 a 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 domains: lung cancer staging, teamwork management, and trolley problems.
|Number of pages||18|
|Publication status||Published - 13 Dec 2019|
|Event||Knowledge Representation & Reasoning Meets Machine Learning: Workshop at NeurIPS'19 - Vancouver, Canada|
Duration: 13 Dec 2019 → 13 Dec 2019
|Workshop||Knowledge Representation & Reasoning Meets Machine Learning|
|Period||13/12/19 → 13/12/19|
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