Ever had an app recommend something completely random, leaving you wondering, “Why on earth did it show me that?” Maybe Netflix suggested a rom-com when you’ve been binging crime documentaries, or your banking app flagged a perfectly normal purchase as suspicious. That moment of confusion – that disconnect between what the AI does and why it does it – is the enemy of good AI UX.
In our post on the 5 Core Principles of Effective AI UX, we identified “Be Transparent & Explainable” as the first and most critical principle. Now, we’re going to do a deep dive into exactly how to put that principle into practice.
What Is Explainable AI?
Simply put, Explainable AI (XAI) is the practice of designing AI systems so that human users can understand their decisions and outputs. It’s the antidote to the “black box” where data goes in, a decision comes out, and no one knows what happened in between.
This blog will give you actionable design patterns, strategies, and best practices to create AI experiences that are clear, trustworthy, and user-centric. Let’s get into it.
The Business Case for Clarity: More Than Just ‘Nice to Have’
You might be thinking, “Do we really need to explain everything the AI does?” The short answer: yes, but not because it’s trendy. There are solid business reasons to invest in XAI design.
Building Foundational User Trust
Trust isn’t a feature you can add with a button – it’s an outcome. When users understand the “why” behind an AI’s decision, they’re far more likely to trust the “what.” Without that understanding, even accurate AI recommendations feel arbitrary or suspicious. Trust comes from transparency, and transparency comes from good design.
Empowering Users to Make Informed Decisions
When AI helps with high-stakes decisions – think finances, healthcare, or legal matters – explanations aren’t optional. They’re essential. A user needs to know why the AI is recommending refinancing their mortgage or flagging a health symptom as serious. Without context, they can’t properly weigh the recommendation against their own judgment and circumstances.
Enabling Meaningful Feedback and Control
If a user doesn’t know why an AI got something wrong, they can’t give effective feedback to correct it. But when you provide clear explanations, users become collaborators. They can tell you, “Actually, I didn’t like that suggestion because…” and help improve the system. XAI turns passive users into active partners in making your AI better.
Meeting Ethical and Regulatory Standards
Regulations like GDPR include a “right to explanation,” meaning users can legally demand to know how automated decisions about them were made. XAI isn’t just a nice-to-have design choice; it’s increasingly a business and legal necessity.
4 Actionable Patterns for Designing Clear AI Explanations
Let’s get practical. Here are four proven design patterns you can use to make your AI more understandable, with real examples of when and how to use each one.
Pattern 1: Input-Based Explanations (“Because you…”)
What it is: The most straightforward method – connecting an AI output directly to a specific user action or input.
Best for: Recommendation engines in e-commerce, streaming services, and social media.
Example: “Because you bought a new camera, we recommend this lens.” Or, “Because you watched three true crime documentaries this week, you might like this mystery series.”
This pattern works because it’s intuitive. Users can immediately trace the line from their behavior to the recommendation. It feels personal without being creepy, and it helps users understand that the AI is responding to their actual preferences, not making random guesses.
Pattern 2: Feature-Based Explanations (Highlighting Key Data)
What it is: Showing users which specific data points were most influential in the AI’s decision.
Best for: Classification and scoring systems like loan applications, credit checks, or email spam filters.
Example: A loan application interface might highlight “Credit Score: 750” and “Debt-to-Income Ratio: 25%” as the top two factors that led to an approval. Or an email client might show “Suspicious sender domain” and “Contains phishing keywords” as reasons a message was flagged.
This approach demystifies the decision-making process. Instead of a simple yes/no answer, users see the factors that mattered most. It’s especially valuable in situations where users might want to dispute or appeal a decision – they know exactly what influenced it.
Pattern 3: Counterfactual Explanations (“If you had…”)
What it is: Explaining why a certain outcome was reached by showing what would need to change to get a different result.
Best for: Decision support and coaching tools where users want to improve or change outcomes.
Example: “Your application would have been approved if your credit score was 30 points higher.” Or, “This job posting would reach 40% more candidates if you increased the salary range.”
Counterfactuals are powerful because they’re actionable. They don’t just explain what happened – they show users a path forward. This pattern works particularly well when you want to empower users to improve their results over time.
Pattern 4: Contextual & Real-Time Explanations
What it is: Providing small, just-in-time explanations through UI elements like tooltips, info icons, or expandable sections.
Best for: Complex interfaces where a full explanation isn’t always needed, but should be available when users want it.
Example: Hovering over a suggested social media post time and seeing a tooltip that says, “Recommended based on when your audience was most active this week.” Or clicking an info icon next to an AI-generated summary to see which sources it drew from.
This pattern respects user attention. Not everyone wants or needs a detailed explanation every time, but making it available on-demand gives users control. They can dig deeper when they’re curious or skeptical, and skip it when they’re confident in the AI’s output.
Weaving Explainability into Your Product Lifecycle
Great XAI isn’t just a UI layer you slap on at the end. It’s a core component of a user-focused product strategy, and it needs to be integrated from the very beginning.
That means involving designers in early conversations about what the AI will do and how it will work. It means having data scientists and UX researchers collaborate to identify which explanations are both technically possible and actually useful to users. And it means testing explanations with real users throughout development, not just after launch.
To understand how explainability fits into the broader picture of AI UX research, design, and measurement, read our Ultimate Guide to User Experience for AI Solutions.
When you treat XAI as a foundational principle rather than an afterthought, you build products that users actually understand and trust. And that makes all the difference.
Wrapping Up
At its core, XAI is a design challenge focused on turning data into a meaningful dialogue with users. It’s about respecting their intelligence, their autonomy, and their right to understand the tools they’re using.
The goal of Explainable AI isn’t just to make AI understandable – it’s to make AI a better partner for human intelligence. When done right, XAI doesn’t just improve trust; it improves outcomes, collaboration, and the overall user experience.
Frequently Asked Questions
What’s the difference between explainability and interpretability?
Interpretability is about how well a data scientist can understand the model’s internal mechanics – the algorithms, weights, and mathematical operations. Explainability is about how well a user can understand the model’s output and decisions, regardless of the underlying complexity. You can have an interpretable model that gives terrible explanations to users, and vice versa. For design purposes, we care most about explainability.
Does every AI feature need a detailed explanation?
No – the level of explanation should match the stakes. A movie recommendation needs less justification than a medical diagnosis or a loan rejection. The key is to match the detail to the user’s need and the decision’s impact. Low-stakes, frequent decisions can have lighter explanations, while high-stakes, infrequent decisions deserve more depth.
How can designers work with data scientists on XAI?
Start early! Designers can create user personas and journey maps to show when and where explanations are needed in the user experience. Data scientists can identify what aspects of the model can be explained and how. It’s a true partnership – designers bring the user perspective, and data scientists bring the technical constraints. Regular collaboration sessions throughout development are key.
How do you test if an explanation is effective?
Through user testing. Ask users questions like, “Can you tell me why the system suggested this?” or “What would you do next based on this information?” Their ability to answer accurately indicates whether the explanation is working. You can also measure whether users who see explanations make different decisions or express more confidence than those who don’t.
Where’s the best place to start improving XAI in an existing product?
Start with the most high-impact, low-effort area. Often, this is implementing simple Input-Based Explanations (“Because you…”) for recommendations. It provides immediate value to users, doesn’t require complex technical work, and can be rolled out quickly. Once you’ve built momentum there, you can tackle more sophisticated explanation patterns.
