How to develop trust in human AI interactions

AI systems are increasingly powerful. Trust in them rarely depends on the quality of the model. The decisive factor is how understandable, predictable, and controllable the interface appears to humans. There is a principle that seems ubiquitous: AI makes the preliminary decisions, humans bear the responsibility. This creates an area of tension that we must consciously shape. Trust is therefore both a task and a result of interface design.

How an interface develops trust

Research teams such as Google PAIR, Microsoft Research, and the Human AI Interaction Group at the University of Washington unanimously emphasize three decisive factors for the development of trust in AI: an understandable reasoning, visual transparency on how reliable the result is, and accessible options to review and control it. This raises three key questions for users before they are willing to accept an AI-generated recommendation:

1. Why does this suggestion appear?
2. How reliable is it?
3. What will happen and can I intervene?

The AI model provides output, but does not answer these questions. The answers must appear where the decisions are made in the interface. This can be demonstrated quite easily using an example solution.

Our example: automated suggestions in invoice matching

Our example is an accounting solution: The AI assistant suggests matches between invoices and orders. These suggestions need context, otherwise they will remain intransparent and therefore a black box to users. To visualize this, let’s first take a look at a state which doesn’t answer our three key questions in the interface:

We will expand on this example step by step until mere suggestions become an interface worth trusting. Let’s start with the first principle behind our key questions:

Why does this suggestion appear?

Explainability is most effective when placed where decisions are made. We avoid outsourced info panels and locate short, verifiable annotations directly below the suggestion. In our example, the AI assistant checks and visualizes the match between the item number and supplier, whether the price is within tolerance, and provides references to order data, receipts, and legal guidelines. This allows users to understand and compare the suggestion without leaving its context.

How reliable is this suggestion?

A visible assessment of the AI model’s confidence in a suggestion is essential for users to correctly interpret it. Using percentages is more technical than helpful. Information such as the confidence level on an easily understandable scale — high, medium, low — or a short explanation on e.g. the data, the availability of goods receipts, or a contradictory history creates realistic expectations and prevents misinterpretations.

What will happen and can I intervene?

Transparency and control are equally important when designing AI solutions. An AI assistant’s support must remain predictable for users. The interface has to clearly show what the AI assistant will do, what effects this step will have, how the step can be corrected or parameters adjusted, and that each change can be undone. Users do not need to intervene constantly, but they need to know that they can.

Why these principles cultivate trust

Our three key questions ensure that AI-suggested decisions are not blindly greenlit but remain verifiable, traceable, and controllable. The result: errors in operation are reduced, user inquiries are answered early on, and everyday acceptance in use increases.

Combining the established functions and design elements for our example creates an interaction that genuinely inspires trust:

As a designer, I see fearing the black box as the biggest obstacle in trusting AI solutions. Humans are most likely to accept AI suggestions when it is clear where the generated information stems from and why it is plausible. This is why I treat AI suggestions like any other source of information that requires verification. Equally important is a sympathetic approach to the concern the AI assistant may act above and beyond human control. The interface must clearly establish that the AI assistant works with and for the user.

Trust in AI systems is not an abstract goal, but a concrete design task that is rewarded with the decision to rely on its results. When an interface explains why a suggestion appears, how reliable it is, and what will happen next, it serves to translate and illustrate technical processes in a comprehensible way - no matter how complex the AI model is working in the background.