Year: 2023
Type: preprint
Abstract: Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction. The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, etc. In this paper, we are interested in the notion of robustness of a counterfactual. More precisely, we focus on robustness to counterfactual input changes. This form of robustness is particularly challenging as it involves a trade-off between the robustness of the counterfactual and the proximity with the example to explain. We propose a new framework, CROCO, that generates robust counterfactuals while managing effectively this trade-off, and guarantees the user a minimal robustness. An empirical evaluation on tabular datasets confirms the relevance and effectiveness of our approach.
Source: arXiv (Cornell University)
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Orange (France), Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes, AISTROSIGHT: La pharmacologie des neurones et des astrocytes à l’aide des sciences du numérique
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4367061045