Persuasive contrastive explanations

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.