Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language.

Salimi, Pedram

Abstract

Counterfactual explanations have become a significant component in eXplainable AI (XAI), offering intuitive "what if" scenarios. However, typical numeric or tabular outputs can be vague to non-technical audiences. Additionally, many counterfactual methods ignore causal relationships or suggest inactionable changes such as "be younger by five years," raising concerns over realism and ethics. To address these issues, we propose a holistic approach that integrates a Feature Actionability Taxonomy (FAT) and causal discovery into counterfactual generation, thereby ensuring realistic, ethically sound, and semantically transparent explanations in natural language. We further introduce an interactive, agentic workflow enabling users to iteratively refine constraints. Through extensive user studies, pilot evaluations, and synergy with Case-Based Reasoning (CBR), this approach yields explanations that are accessible, trust-enhancing, and practically useful in domains such as healthcare, finance, and education.

Citation

SALIMI, P. 2025. Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language. In Martin, K. and Ye, X. (eds.) ICCBR-WS 2025: joint proceedings of the workshops and doctoral consortium at the 33rd International conference on case-based reasoning (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June 2025, Biarritz, France. CEUR workshop proceedings, 3993. Aachen: CEUR-WS [online], pages 95-99. Available from: https://ceur-ws.org/Vol-3993/short9.pdf

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 33rd International conference on case-based reasoning workshops and doctoral consortium (ICCBR-WS 2025) co-located with the 33rd International conference on case-based reasoning (ICCBR 2025)
• Start Date: Jun 30, 2025
• Acceptance Date: Apr 6, 2025
• Online Publication Date: Jun 12, 2025
• Publication Date: Jul 8, 2025
• Deposit Date: Aug 1, 2025
• Publicly Available Date: Aug 1, 2025
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Pages: 95-99
• Series Title: CEUR-workshop proceedings
• Series Number: 3993
• Series ISSN: 1613-0073
• Book Title: ICCBR-WS 2025
• Keywords: Explainable AI; Counterfactual explanations; Causality; Natural language generation
• Public URL: https://rgu-repository.worktribe.com/output/2959176
• Publisher URL: https://ceur-ws.org/Vol-3993/