The Impact of Explanations on Layperson Trust in Artificial Intelligence-Driven Symptom Checker Apps: Experimental Study

TL;DR

This study investigates how different explanations in AI symptom checkers influence laypeople's trust, emphasizing the importance of personalized explanations tailored to users' disease knowledge to improve trust and effectiveness.

Contribution

It provides empirical evidence that personalized, context-aware explanations enhance trust in AI symptom checkers, highlighting the need for tailored communication strategies.

Findings

Explanations impact trust levels among laypeople.

Personalized explanations improve user trust.

User knowledge influences explanation effectiveness.

Abstract

To achieve the promoted benefits of an AI symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople. This study ascertains whether explanations provided by a symptom checker affect explanatory trust among laypeople (N=750) and whether this trust is impacted by their existing knowledge of disease. Results suggest system builders developing explanations for symptom-checking apps should consider the recipient's knowledge of a disease and tailor explanations to each user's specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.