Have you ever browsed your favourite streaming service and thought, Why on earth is this show being recommended to me? Or asked ChatGPT a question and wondered, Hmm…is this response really accurate?
Those questions are ultimately about trust, and they tend to arise when we don’t fully understand how an AI system works or how it reached a particular outcome.
And, while it might seem trivial on the surface, those small moments of uncertainty can actually have a huge impact on the user experience.
Designing for trust has always been an essential part of UX, but AI raises the stakes. Understandably, people approach AI with even more caution and skepticism. This means designers must be even more intentional about reducing uncertainty and building user confidence.
And therein lies a huge UX challenge: How do you package the increasingly powerful capabilities of AI into an interface that feels transparent and trustworthy?
In this guide, we’ll show you how to design AI interfaces that earn your users’ trust. We’ll cover:
- What makes an AI interface feel trustworthy?
- 7 best practices for designing trustworthy AI interfaces (with examples)
- Key takeaways and further resources
Let’s begin.
What makes an AI interface feel trustworthy?
Every interaction with an AI-powered product has the potential to create uncertainty. Naturally, your users want to know what’s happening and why, and they want to feel that they still have some control.
If you want to design for trust, you’ve got to focus on reducing uncertainty and increasing transparency.
This means anticipating the kinds of questions your users will have, and building the answers directly into the interface. Questions like:
- What is the AI doing and why?
- Can I trust this particular response or outcome?
- What information is this based on?
- Am I still in control?
- What happens if something goes wrong?
The less your users have to wonder, the more they’ll trust your product. And that’s what the following best practices are all about: creating AI interfaces that feel transparent, predictable and trustworthy.
7 best practices for designing trustworthy AI interfaces
1. Explain AI recommendations and decisions
It’s difficult to fully trust AI if we’re not sure why it produced a particular recommendation or response. How do we know it’s not just making something up completely randomly?
Instead of expecting your users to simply accept the AI’s output, explain the reasoning behind it in plain language. A short, contextual explanation is often enough to reassure users that the system is behaving as expected.
Let’s say you’re designing an online learning platform that uses AI to make course recommendations.
Rather than displaying a generic label like “Recommended for you”, the interface could say something like “Recommended for you because you recently completed our UX Fundamentals course” or “Consider these courses next to complete your French for Beginners certification”.
Those small explanations transform the recommendation from something that feels arbitrary into something users can understand and evaluate.
2. Use visual cues to communicate confidence levels
AI doesn’t always have the same level of confidence in every prediction, recommendation or response, and that should be communicated to the user.
Visual confidence indicators help users judge how much weight they should give an AI-generated output. Rather than replacing human judgement, they support it.
Imagine a healthcare app that uses AI to assess a patient’s symptoms and suggest possible diagnoses. Instead of saying, with 100% confidence, “You have seasonal allergies”, a more trustworthy approach could be something like:
- High confidence (depicted by a visual percentage bar or a green traffic light icon, for example): seasonal allergies
- Medium confidence: common cold
- Low confidence: asthma. Consider speaking to a healthcare professional if symptoms persist.
The goal isn’t to overwhelm users with technical scores or probabilities. It’s to communicate uncertainty in a way that’s easy to understand and helps users make informed decisions.
3. Show the evidence behind AI-generated outputs
One of the quickest ways to build trust is to show users where the AI got its information from.
Think about how many people now use AI to answer questions instead of searching Google. Whether they’re researching a medical condition, comparing mortgage rates or learning about a new topic, they often want to dig a little deeper or double-check what they’re reading.
Imagine you’re designing an AI research assistant that summarises long reports. Rather than simply presenting a list of key takeaways, the interface could link each point back to the relevant section of the original document or include citations to the sources it used.
That way, users can quickly verify the information for themselves and check that the AI hasn’t misunderstood something. Instead of asking users to simply trust the AI, you’re giving them the evidence they need to build that trust themselves.
4. Give users meaningful control
Even the best AI tools won’t always produce exactly what the user was hoping for. That’s why it’s so important to allow users to refine and challenge the AI’s output.
Rather than treating AI-generated content as the final answer, think about how users can stay involved throughout the process. The more control they have, the more confident they’ll feel using the product.
Take an AI writing assistant for example. After generating a first draft, you might give users the option to “Regenerate”, “Make it shorter”, “Change the tone”, or simply edit the text themselves. Those options make it quick and easy to improve the output without having to start again from scratch.
Ultimately, AI should feel like a collaborator rather than a replacement. Giving users meaningful control helps them get to a better end result while reinforcing that they’re still the one making the final decisions.
5. Make AI activity visible
When AI is working behind the scenes, it’s easy for users to feel like they’re waiting for something to happen without really knowing what’s going on. That uncertainty can quickly undermine trust, especially if the task takes more than a few seconds.
Instead of hiding the AI’s activity behind a loading spinner, show users what it’s doing. Even simple progress updates can reassure users that the system is working as expected.
Imagine you’re designing an AI travel planner that creates a personalised itinerary. Rather than displaying a generic “Generating your trip…” message, the interface could show each stage of the process as it happens, with ticks and sand-timer icons to show what’s been completed and what’s still in progress:
- Finding suitable flights ✅
- Comparing hotel options ✅
- Building your itinerary ⏳
- Recommending restaurants and activities ⏳
This helps manage expectations and reassures the user that their request is in progress. The experience feels less like a black box and more like a transparent process they can follow.
6. Design for recovery when AI goes wrong
AI will inevitably make mistakes, so it’s crucial to make it easy for users to get back on track.
If an AI gives an incorrect answer or misunderstands what the user is trying to do, they should never feel stuck. Instead, the interface should offer a clear path forward, whether that’s editing the request, trying again or speaking to a human.
Let’s say you’re designing a customer support chatbot. A customer asks about returning an item, but the AI mistakenly assumes they’re asking about an exchange. Rather than forcing the user to start the conversation from scratch, the chatbot could offer options such as “Try again”, “Rephrase your question” or “Chat with a support agent”.
Recovery features like these help turn frustrating moments into manageable ones. More importantly, they reassure users that even if the AI gets something wrong, they won’t be left without a solution.
7. Ask for permission before personalising experiences
AI can create incredibly personalised experiences, but that doesn’t mean users expect their data to be used in every possible way.
If an AI feature relies on personal information, be upfront about what data you’d like to use, why you’re using it and how it will improve the experience. Giving users a genuine choice helps build trust from the very beginning.
Imagine you’re designing a fitness app that uses AI to create personalised workout plans. Rather than automatically analysing a user’s activity history, the app could ask for permission first, explaining that their workout history will be used to recommend training plans tailored to their goals. It should also make it easy for users to change this preference later if they wish.
When users understand how their data is being used and feel they’re in control of that decision, personalisation feels helpful rather than intrusive.
The takeaway
Designing for trust is really about designing for transparency and understanding. Once users understand what’s happening and why, and what control they have, they’ll feel much more confident putting their trust in an AI product.
With the seven best practices we’ve outlined, you can proactively address your users’ concerns and build trust directly into the product interface.
Keep building your AI skills for UX
Learning how to design user-friendly, trustworthy AI experiences is just one side of the coin. More and more, UX designers must also understand how AI tools work and be able to confidently integrate them into their workflow.
With our AI course series, you’ll cover practical ways to use AI in your day-to-day work. You’ll learn how to use emerging AI tools effectively, write better prompts and balance speed and automation with your human expertise.
You can learn more about our courses here:
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For more industry guides and insights, check out these posts:
- 11 AI prompts every designer should save
- How to design effective AI search experiences
- A complete AI for UX glossary: 100 terms all designers should know
Emily is a professional writer and content strategist with an MSc in Psychology. She has 8+ years of experience in the tech industry, with a focus on UX and design thinking. A regular contributor to top design publications, she also authored a chapter in The UX Careers Handbook. Emily also holds a BA in French and German and is passionate about languages and continuous learning.
