An ongoing challenge in machine learning is to improve the transparency of learning models, helping end users to build trust and defend fairness and equality while protecting individual privacy and information assets. Transparency is a timely topic given the increasing application of machine learning techniques in the real world, and yet much more progress is needed in addressing the transparency issues. We propose critical research questions on transparency-aware machine learning on two fronts: know how and know that. Know-how is concerned with searching for a set of decision objects (e.g. functions, rules, lists, and graphs) that are cognitively fluent for humans to apply and consistent with the original complex model, while know-that is concerned with gaining more in-depth understanding of the internal justification of the decisions through external constraints on accuracy, consistency, privacy, reliability, and fairness.
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