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

arXiv: https://arxiv.org/abs/2202.13444 · HTML: https://mlsanity.com/paper/2202.13444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Towards Better Health Conversations: The Benefits of Context-seeking](https://mlsanity.com/paper/2510.18880/) (2025)
- [Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers](https://mlsanity.com/paper/2604.20166/) (2026)

## Abstract (arXiv)

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.