I’ve been looking into how we can make user perceptions of AI systems more positive when it comes to trust. This is particularly crucial in fields like healthcare, finance, or any area where AI decisions have a real impact on people’s lives. Here’s a top level view of that. I have used Google’s People + AI and my own experience as a designer at Faculty as key references here.
1. Calibrate Trust
First off, transparency is the name of the game. Users need to know where the data is coming from. Let’s say you’re using an AI tool for medical diagnosis; wouldn’t you want to know which database or model it’s pulling information from?
- Show Data Sources: Clearly indicate the origin of the data. If your AI is recommending a treatment plan, show it’s based on up-to-date, peer-reviewed research or specific datasets.
- Dynamic Visualizations: Link explanations directly to user actions. Imagine you’re adjusting settings for a financial AI tool, and it instantly shows you how each tweak affects outcomes through graphs or charts. Read more about Dynamic Interaction
- High Stakes, High Clarity: For decisions with significant consequences, involve the user. Ask them to verify or review the AI’s rationale. For instance, if an AI suggests a life-altering medical procedure, offer a detailed explanation of why that path was chosen.
2. Education and Onboarding (but not too much)
Educating users about how AI works in your system can help settle nerves. A good onboarding process can demystify AI, making users feel more in control. However it really should be a secondary approach – users don’t want to read reams of documentation, they want to dive in and try things.
- Start with simple explanations and gradually introduce more complex concepts as users become more comfortable through progressive disclosure.
3. When Explanations Aren’t Enough
Sometimes, AI decisions are too complex to explain fully. Here’s where we can get creative:
- Natural Language Explanations: Use LLMs (Large Language Models) to provide explanations in plain English. If an AI decides against approving a loan, it could explain in layman’s terms why – like “The decision was based on your credit history and current debt levels.”
4. Confidence Levels
Understanding the AI’s confidence in its decisions is key:
- Simplify Confidence Indicators: Not everyone is a data scientist. Use simple systems like percentages or even a traffic light system (red for low confidence, green for high) to convey trust levels. Even so. What’s the difference between 65% and 66% confidence to most people?
- Progressive Disclosure for Pros: For those who want to dive deeper, provide layered information. Let expert users peel back layers to see more about how the confidence was calculated.
Further ideas:
- Does Showing Confidence Help? We need to look into how different levels of detail in confidence indicators affect user trust. Is a simple percentage enough, or do we need to go into specifics like confidence intervals?
- Granularity of Information: How much detail is too much? Finding the sweet spot where users feel informed but not overwhelmed will be crucial.
Designing for trust isn’t just about making AI work; it’s about making AI work with people. By focusing on transparency and clear communication of AI’s decision-making process, we can build applications that not only perform well but are also embraced by those they’re meant to help.
Thanks for reading. Let me know what you think.