RT Journal Article
T1 Introducing User Feedback-based Counterfactual Explanations (UFCE)
A1 Suffian, Muhammad
A1 Alonso Moral, José María
A1 Bogliolo, Alessandro
K1 Explainable Artificial Intelligence
K1 Human-centred Explanations
K1 Interpretable Machine Learning
K1 Counterfactual Explanations
K1 User Feedback
AB Machine learning models are widely used in real-world applications. However, their complexity makes it often challenging to interpret the rationale behind their decisions. Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in eXplainable Artificial Intelligence (XAI). CE provides actionable information to users on how to achieve the desired outcome with minimal modifications to the input. However, current CE algorithms usually operate within the entire feature space when optimizing changes to turn over an undesired outcome, overlooking the identification of key contributors to the outcome and disregarding the practicality of the suggested changes. In this study, we introduce a novel methodology, that is named as user feedback-based counterfactual explanation (UFCE), which addresses these limitations and aims to bolster confidence in the provided explanations. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features while considering feature dependence, and evaluates the practicality of suggested changes using benchmark evaluation metrics. We conducted three experiments with five datasets, demonstrating that UFCE outperforms two well-known CE methods in terms of proximity, sparsity, and feasibility. Reported results indicate that user constraints influence the generation of feasible CEs.
PB Springer
YR 2024
FD 2024-05-21
LK https://hdl.handle.net/10347/47342
UL https://hdl.handle.net/10347/47342
LA eng
NO Suffian, M., Alonso-Moral, J.M. & Bogliolo, A. Introducing User Feedback-Based Counterfactual Explanations (UFCE). Int J Comput Intell Syst 17, 123 (2024). https://doi.org/10.1007/s44196-024-00508-6
NO This work is funded by MCIN/AEI/10.13039/501100011033 (grants PID2021-123152OB-C21, TED2021-130295B-C33 and RED2022-134315-T), the “European Union NextGenerationEU/PRTR”, and by the Galician Ministry of Culture, Education, Professional Training and University (grants ED431G2019/04 and ED431C2022/19). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program). This work is partially funded by MIMIT, under FSC project “Pesaro CTE SQUARE”, CUP D74J22000930008.
DS Minerva
RD 4 jun 2026