Evaluation of Human-Understandability of Global Model Explanations using
  Decision Tree
Adarsa Sivaprasad Ehud Reiter Nava Tintarev Nir Oren
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.