Evaluation of Human-Understandability of Global Model Explanations using Decision Tree

TL;DR

This study investigates how narrative, patient-specific global explanations of AI models improve understandability for non-expert users in healthcare, emphasizing the importance of explanation type preferences and mental models.

Contribution

It introduces a method to generate and evaluate global and local explanations for healthcare AI models tailored for non-expert users, highlighting user preferences and mental models.

Findings

Majority prefer global explanations

Smaller group prefers local explanations

Global explanations enhance trust and decision-making

Abstract

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.