RT Journal Article
T1 The Role of User Feedback in Enhancing Understanding and Trust in Counterfactual Explanations for Explainable AI
A1 Suffian, Muhammad
A1 Kuhl, Ulrike
A1 Bogliolo, Alessandro
A1 Alonso Moral, José María
K1 Explainable AI
K1 Human-centered explanations
K1 Counterfactual explanations
K1 Human behavioral analytics
K1 User study
AB Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in the context of explainable artificial intelligence (XAI). A CE provides actionable information to users on how to achieve the desired outcome from a machine learning (ML) model with minimal modifications to the input. XAI is crucial for improving transparency and reliability in AI systems, especially for meeting regulations like the General Data Protection Regulation (GDPR) or the European AI Act. However, the integration of CEs into XAI frameworks and their effectiveness in enhancing user trust and cognitive learning remains uncertain and requires further research. We have developed a user study to face this challenge with two user input-driven counterfactual generation XAI approaches: (i) User Feedback-based Counterfactual Explanation (UFCE) and (ii) Diverse Counterfactual Explanation (DiCE). They are integrated within a game-inspired online platform that enables direct comparisons between them.We compared the task performance, understanding, satisfaction, and trust between control and experimental groups, with a total of 101 participants. After curating the collected data, we had 70 users (24 in the control group) who successfully completed the experiment. Participants in the experimental group received explanations generated by UFCE or DiCE. Findings show that explanations generated by UFCE improve users’ learning experiences, resulting in better task performance, comprehension, satisfaction, and trust. Moreover, participants who interacted with UFCE exhibited significantly higher reliance on suggestions than those who interacted with DiCE, what was supported by statistical validation. These results highlight the significance of human-centered XAI methods and promote meaningful cognitive engagement for users.  Furthermore, the game-inspired platform is implemented as open-source to promote Open Science, and it is made publicly available along with data collected in the user study to support further investigations and to ensure reproducibility of reported results.
PB Elsevier
YR 2025
FD 2025-03-14
LK https://hdl.handle.net/10347/47344
UL https://hdl.handle.net/10347/47344
LA eng
NO This work was partially funded by MIMIT, Italy, under FSC project “Pesaro CTE SQUARE”, CUP D74J22000930008. Jose Maria Alonso-Moral was supported by MCIN/AEI/ 10.13039/501100011033 (grant PID2021-123152OB-C21), but also by the Galician Ministry of Culture, Education, Professional Training, and University, Spain (grants ED431C2022/19 and ED431G2019/04), all grants were co-funded by the European Regional Development Fund (ERDF/FEDER program). Ulrike Kuhl was supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia, Germany .
DS Minerva
RD 4 jun 2026