Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse
Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input features were manipulated. This technique is used for a range of tasks such as debugging models, explaining predictions, justifying decisions, and providing algorithmic recourse. In this paper, we explore the normative legitimacy of employing counterfactuals in real-life model deployment settings. We discuss the different stakes involved in these different purposes for which CEs are commonly employed, and find stricter requirements for justification and recourse. In particular, we find that naive application of CEs for justification and reco...
| Authors | Mattia Cerrato, Otto Sahlgren, Xenia Heilmann |
|---|---|
| Primary category | cs.CY |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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