The Impact of Explanations on Layperson Trust in Artificial Intelligence-Driven Symptom Checker Apps: Experimental Study
This addresses the problem of low trust and adoption of AI symptom checkers among laypeople, offering incremental insights for app developers.
The study investigated how different types of explanations affect laypeople's trust in AI-driven symptom checker apps, finding that personalized explanations tailored to users' disease knowledge are crucial for building trust.
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.