Persuasive contrastive explanations

Tara Koopman, Silja Renooij

    Research output: Contribution to conferencePaperAcademic

    Abstract

    Explanation in Artificial Intelligence is often focused on providing reasons for why a model under consideration and its outcome are correct. Recently, research in explainable machine learning has initiated a shift in focus on including so-called counterfactual explanations. In this paper we propose to combine both types of explanation into a persuasive contrastive explanation that aims to provide an answer to the question Why outcome t instead of t'? posed by a user. In addition, we propose a model-agnostic algorithm for computing persuasive contrastive explanations from AI systems with few input variables.
    Original languageEnglish
    Pages1-6
    Number of pages6
    Publication statusPublished - 2021
    EventXLoKR 2021 -
    Duration: 3 Nov 20215 Nov 2021

    Conference

    ConferenceXLoKR 2021
    Period3/11/215/11/21

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