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Explaining predictions with enthymematic counterfactuals

Abstract:
When people are subject to high-stakes decisions informed by computer models, they have a legitimate interest in understanding the basis for the model’s judgements and whether actions can be taken to turn a dispreferred decision into a preferred one. For example, if an application for a loan is denied by the model, the applicant has an interest in understanding the conditions that would yield an approval. In this paper, we argue that these kinds of counterfactual (or contrastive) explanations rest on domain-specific and commonsensical principles that can be negotiated, and sketch a method for incorporating such principles in an explanatory dialogue system using enthymematic reasoning.
Research areas:
Year:
2022
Type of Publication:
In Proceedings
Book title:
Proceedings of BEWARE Workshop at AIxIA
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