The challenges of providing explanations of AI systems when they do not behave like users expect

Abstract

Explanations in artificial intelligence (AI) ensure that users of complex AI systems understand why the system behaves as it does. Expectations that users may have about the system behaviour play a role since they co-determine appropriate content of the explanations. In this paper, we investigate user-desired content of explanations when the system behaves in unexpected ways. Specifically, we presented participants with various scenarios involving an automated text classifier and then asked them to indicate their preferred explanation in each scenario. One group of participants chose the type of explanation from a multiple-choice questionnaire, the other had to answer using free text.

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