Meaningful Explanation Effect on User’s Trust in an AI Medical System: Designing Explanations for Non-Expert Users
Retno Larasati, Anna De Liddo, Enrico Motta
Abstract
Whereas most research in AI system explanation for healthcare applications looks at developing algorithmic explanations targeted at AI experts or medical professionals, the question we raise is: How do we build meaningful explanations for laypeople? And how does a meaningful explanation affect user’s trust perceptions? Our research investigates how the key factors affecting human-AI trust change in the light of human expertise, and how to design explanations specifically targeted at non-experts. By means of a stage-based design method, we map the ways laypeople understand AI explanations in a User Explanation Model. We also map both medical professionals and AI experts’ practice in an Expert Explanation Model. A Target Explanation Model is then proposed, which represents how experts’ practice and layperson’s understanding can be combined to design meaningful explanations. Design guidelines for meaningful AI explanations are proposed, and a prototype of AI system explanation for non-expert users in a breast cancer scenario is presented and assessed on how it affect users’ trust perceptions.