Abstract
This study introduces a Z-numbers-based Weighted Sum Model (WSM) tailored to evaluate user satisfaction with explanations provided by Explainable Artificial Intelligence (XAI) methods in the healthcare domain. Focusing on the interpretability of XAI, we measure how users perceive the adequacy of explanations through the lens of SHapley Additive exPlanations (SHAP), Individual Conditional Expectation (ICE) plots, and Counterfactual Explanations (CFE). By conducting interviews with healthcare professionals, we integrate their qualitative feedback with quantitative analysis to assess the effectiveness of these methods. The results present a user-centric perspective on the clarity, relevance, and trustworthiness of the generated post-hoc explanations. This study advances the fields of information sciences and healthcare by offering a systematic approach for evaluating XAI, enhancing the transparency and reliability of AI in critical decision-making processes.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
