Short answer

When designing AI-driven health tools, go beyond simply stating AI is used; provide clear, understandable explanations of the AI's function and reasoning to build user trust, but acknowledge that trust alone may not suffice for critical decisions.

Field: Innovation & Design
Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2023)
Method: Simulated study
Evidence: Moderate effect

Users perceive AI health applications as more accurate and trustworthy when their descriptions include explanations, though this alone does not guarantee reliance for critical health decisions. This innovation & design research insight is drawn from a 2023 study published in Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. Using Simulated study, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing AI-driven health tools, go beyond simply stating AI is used; provide clear, understandable explanations of the AI's function and reasoning to build user trust, but acknowledge that trust alone may not suffice for critical decisions.

Study

Innovation & DesignRecentModerate effect

Explanations Enhance AI Trustworthiness in Health Apps, But High-Stakes Decisions Require More Than Transparency

Users perceive AI health applications as more accurate and trustworthy when their descriptions include explanations, though this alone does not guarantee reliance for critical health decisions.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2023

Key Findings

01Participants preferred AI descriptions that included explanations.
02Explanations led to perceptions of greater accuracy and trustworthiness.
03Despite increased trust, participants were hesitant to rely on AI for high-stakes health goals due to potential failure consequences.

Application

Design takeaway

How to apply

When developing an AI feature for a health app, create a dedicated section or tooltip that clearly explains the AI's purpose, how it processes data, and the basis for its recommendations. Test these explanations with target users.

Project actions

01When describing your AI in your design project, focus on clarity and simplicity.
02Consider how your design addresses potential user fears about AI errors, especially if the product has high stakes.

Method & Evidence

AimTo investigate how different descriptions of AI influence user attitudes towards algorithmic recommendations in fertility self-tracking applications.

MethodSimulated study

ProcedureParticipants interacted with three versions of a simulated fertility app, each with a different AI description. Their perceptions of accuracy, trustworthiness, and willingness to rely on the app for health decisions were then assessed.

ContextPersonal health applications, fertility tracking

Variables

IVType of AI description (e.g., with explanation vs. without explanation)

DVUser perceptions of accuracy, trustworthiness, and willingness to rely on the app.

CVThe core functionality of the fertility app, the simulated data presented.

Strengths & Limitations

Strengths

+Directly addresses a gap in understanding user perception of AI in health apps.
+Provides actionable insights for designers regarding AI communication strategies.

Limitations

A real-world health app might have more complex data inputs and outputs than a simulated one, potentially altering user trust dynamics. The specific AI algorithms used in a real app could also be a factor.

Reliability & validity

The study's validity is supported by its focus on user perception and the use of comparative conditions. Reliability would depend on the consistency of participant responses across similar conditions and potential replication.

Think critically

How can designers balance the need for AI explanations with the potential for overwhelming users with technical details, especially in time-sensitive health situations?

Design Principles

"Transparency through explanation fosters user trust in AI systems, but the perceived risk associated with a system's failure must be addressed for full adoption in critical domains."

For designers developing AI-powered health tools, understanding user perception is crucial. Simply stating an app uses AI is insufficient; providing clear explanations for how the AI works and its outputs builds user confidence and encourages adoption.

What This Means for Your Design

If you're making an app that uses AI for health advice, telling people *how* the AI works makes them trust it more. But, if it's a really important health decision, they might still be too scared to rely on it completely.

How to use in your project

1.Reference this study when discussing user trust in AI-powered features within your design project, particularly if your design aims to provide recommendations or analysis.

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Quick Cite

Paragraph starter

User perception of AI-driven health applications is significantly influenced by the clarity of AI descriptions. Research indicates that providing explanations for AI outputs enhances perceived accuracy and trustworthiness, which is crucial for user adoption. However, for high-stakes health decisions, users may still exhibit caution due to the potential consequences of AI failure, suggesting that transparency alone may not fully mitigate concerns.

Source

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

journal · 2023

View source

Questions About This Research

What does the research say about explanations enhance ai trustworthiness in health apps, but high-stakes decisions require more than transparency?: When designing AI-driven health tools, go beyond simply stating AI is used; provide clear, understandable explanations of the AI's function and reasoning to build user trust, but acknowledge that trust alone may not suffice for critical decisions. Evidence: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2023).
Why does "Explanations Enhance AI Trustworthiness in Health Apps, But High-Stakes Decisions Require More Than Transparency" matter for design?: For designers developing AI-powered health tools, understanding user perception is crucial. Simply stating an app uses AI is insufficient; providing clear explanations for how the AI works and its outputs builds user confidence and encourages adoption.
How can designers apply this research?: When designing AI-driven health tools, go beyond simply stating AI is used; provide clear, understandable explanations of the AI's function and reasoning to build user trust, but acknowledge that trust alone may not suffice for critical decisions.
What were the main findings?: Participants preferred AI descriptions that included explanations.. Explanations led to perceptions of greater accuracy and trustworthiness.. Despite increased trust, participants were hesitant to rely on AI for high-stakes health goals due to potential failure consequences.
What research method was used?: Simulated study.
How strong is the evidence?: Evidence strength is rated Moderate effect, based on a 2023 journal from Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies.
What should I do differently in my next project?: When developing an AI feature for a health app, create a dedicated section or tooltip that clearly explains the AI's purpose, how it processes data, and the basis for its recommendations. Test these explanations with target users.
What are the limitations?: The study used simulated apps, and the findings may not directly translate to real-world, complex health management scenarios. The specific AI explanations used might also influence results.