Abstract:Machine learning becomes increasingly important to tune or even synthesize the behavior of safety-critical components in highly nontrivial environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limite… Show more
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