Evaluation of Human-Understandability of Global Model Explanations using Decision Tree
This addresses the need for more comprehensible and trustworthy AI explanations in healthcare for patients, though it is incremental as it builds on existing explanation methods.
The study tackled the problem of making AI model explanations understandable to non-expert users, such as patients in healthcare, by testing narrative and global explanations with a decision tree model for coronary heart disease risk, finding that most participants preferred global explanations over local ones.
In explainable artificial intelligence (XAI) research, the predominant focus has been on interpreting models for experts and practitioners. Model agnostic and local explanation approaches are deemed interpretable and sufficient in many applications. However, in domains like healthcare, where end users are patients without AI or domain expertise, there is an urgent need for model explanations that are more comprehensible and instil trust in the model's operations. We hypothesise that generating model explanations that are narrative, patient-specific and global(holistic of the model) would enable better understandability and enable decision-making. We test this using a decision tree model to generate both local and global explanations for patients identified as having a high risk of coronary heart disease. These explanations are presented to non-expert users. We find a strong individual preference for a specific type of explanation. The majority of participants prefer global explanations, while a smaller group prefers local explanations. A task based evaluation of mental models of these participants provide valuable feedback to enhance narrative global explanations. This, in turn, guides the design of health informatics systems that are both trustworthy and actionable.