Abstract
We propose a novel preference-based method for generating counterfactual explanations in AI-powered information systems that explicitly accounts for individual user preferences. To the best of our knowledge, our method is the first preference-based counterfactual generator in this area. We have conducted experiments on three popular datasets (Communities and Crime, German Credit, and Default of Credit Card Clients) with continuous attributes and binary labels. For those experiments, our method is feasible and capable of producing more relevant counterfactuals than other methods. In the given settings, it is also superior in terms of computational efficiency. In all cases we examined, our method outperformed other techniques in terms of preference alignment. These findings demonstrate the potential of preference-aware counterfactual explanation generation to enhance the adaptability and relevance of intelligent information systems, especially in the context of the sustainable integration of AI and automation.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.57
Designing Preference-Aware Counterfactual Explanations for Adaptive Information Systems
We propose a novel preference-based method for generating counterfactual explanations in AI-powered information systems that explicitly accounts for individual user preferences. To the best of our knowledge, our method is the first preference-based counterfactual generator in this area. We have conducted experiments on three popular datasets (Communities and Crime, German Credit, and Default of Credit Card Clients) with continuous attributes and binary labels. For those experiments, our method is feasible and capable of producing more relevant counterfactuals than other methods. In the given settings, it is also superior in terms of computational efficiency. In all cases we examined, our method outperformed other techniques in terms of preference alignment. These findings demonstrate the potential of preference-aware counterfactual explanation generation to enhance the adaptability and relevance of intelligent information systems, especially in the context of the sustainable integration of AI and automation.
Recommended Citation
Szostak, B. & Doroz, R.(2026). Designing Preference-Aware Counterfactual Explanations for Adaptive Information Systems. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.57