Meaningful Explanation Effect on User's Trust in an AI Medical System: Designing Explanations for Non-Expert Users

Larasati, Retno; De Liddo, Anna and Motta, Enrico (2023). Meaningful Explanation Effect on User's Trust in an AI Medical System: Designing Explanations for Non-Expert Users. ACM Transactions on Interactive Intelligent Systems (Early Access).

DOI: https://doi.org/10.1145/3631614

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.

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