Aug 6, 2025
10 min read
Why Users Distrust AI?
Artificial intelligence is no longer a background technology: it actively shapes how people search, learn, plan, and make decisions. Yet the more powerful it becomes, the more opaque it often feels. Users experience impressive results, but rarely understand how the system arrived there.
That gap between capability and legibility is what we talk about when we refer to AI as a black box, and it’s the root of the trust problem in today’s intelligent applications. In other words: users don’t distrust AI because it’s intelligent, more importantly they distrust it because it’s silent.
Relationship between calibration, resolution, and automation capability. Reproduced from Lee and See (2004). Reproduced from Hinze (2023)
Explainability: telling the user what AI is doing and why it’s doing it.
At its core, trust comes from knowing what something is doing and why it’s doing it. In the context of AI, that translates directly into explainability.
As Henner Hinze writes in his analysis of trustworthy AI frameworks, explicability is one of the four ethical cornerstones of any system that hopes to earn genuine user confidence (Hinze, 2023). But it’s not enough to provide occasional “info” tooltips or one-off disclosure statements. In a product context, explainability needs to be woven into the interaction itself.
Users need to see the chain of reasoning emerge as the system is working, not afterwards, and not buried in documentation.
"This is why a growing number of AI products are starting to surface intermediate decision steps, attribution labels, and even “thought” status indicators.They are no longer just providing outcomes, they are showing the process that leads to those outcomes."
Transparency: a rhythm of interaction that bring users into the loop
Designing trustful AI is not just about revealing information, but about creating a rhythm of interaction that reassures the user that the system is listening, thinking and adapting.
Sudarshan Sahu, writing for UX Planet, reminds us that interaction design for AI is fundamentally different from traditional UX. Instead of fixed flows, AI experiences are co-constructed, and therefore must signal intention and progress at each step (Sahu, 2022). That means using visible reasoning paths, quick acknowledgements (like “Got it, starting competitor analysis”), and contextual suggestions.
When done well, these micro-interactions work as a kind of “conversation.” One makes the reasoning behind the AI feel coherent and collaborative, rather than mysterious and unilateral.
Here Comes A New Paradigm of AI UX
If explainability is the foundation of trust, then visibility of reasoning is its architectural expression. Rather than simply surfacing the final output, trustworthy AI interfaces walk users through the logic of the system step-by-step, allowing them to follow the same path the machine takes.
In the following section, we’ll translate these perspectives into practice through a real-world case study. We’ll look at how a multi-agent AI content platform uses interface design to:
Reveal its internal chain of reasoning (step-by-step, in plain language);
Simulate cognitive effort through designed “thinking” states;
Attribute decisions to specific roles and data sources; and
Give users the power to edit the reasoning logic in real time.
Together, these choices show what it means to design trust into the interaction itself, not just explain it after the fact.
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🔎 Case Study - PhotoG: Making AI Reasoning Visible through Role-Based Logic
In the PhotoG product developed by VSDesign, the content-generation process is not delivered as a single “black-box” response. Instead, the platform intentionally decomposes the workflow into four distinct AI agents, each mapped to a different cognitive function.
The interface demonstrates how AI advances content generation through action paths from Step 1 to Step 4. This is one of the typical "visible reasoning" design approaches, explaining to users why the system does it this way:
PhotoG_ Action Plan with 4 AI agent
What makes this approach particularly compelling is its narrative transparency. The interface doesn’t ask the user to “just trust the output.”
Instead, it tells a story of how the result came to be, and in doing so, answers critical user questions at each step. PhotoG provides a great example of how a good UX design solves users' possible question “Where does this information come from?” that they can be aware this product diminishes the blackbox at the beginning.
In PhotoG, the role icons at the top show the current owner of the task, and the chat pane displays a clear “Thinking…” state with “Catching data from internet…” to signal active gathering. The analyst then offers two concrete source paths. On the left, a curated “Trend Report and Reference Posts” card appears with a “Confirm Reference Selection” action. On the right, a structured panel lists trending topics and reveals an explainer for the selected topic, including why it matters and how creators are using it. When the user selects either a trend or a reference post, PhotoG binds that choice to the session, records the provenance, and moves the workflow forward. Step 1 therefore answers the question “Where did this information come from?” before any creative suggestions appear, and it hands the Brand Strategist a documented starting point for further step.
PhotoG_ Provide the original information for AI results
👉 By structuring the interaction as a guided reasoning path, PhotoG transforms an opaque decision process into a transparent, collaborative journey, and trust emerges organically along that path.
A “Thinking…” animated label appears at the top of the interface, accompanied by subtle gray loading text beneath it, simulating the AI agent’s active information-processing stage. While the placeholder text in the design files isn't functional, it shows developers where to integrate the AI's reasoning functions. The design intent is to reduce the AI’s “black box” effect by signaling to the user that the system is performing multi-round data analysis and ideation.
PhotoG_“Thinking…” Status Indicator fold and unfold status
For each generated result (e.g., “Interior Design Industry Research Report 2025” and “Competitive Analysis”), the interface displays a role icon (e.g., a robot symbol) alongside a clear textual structure, indicating which agent produced the content and the reasoning logic behind it.
This approach supports a traceable content generation chain, enabling users to backtrack each output to its corresponding reasoning step.Instead of “Here is the report,” the UI effectively says “Here is the report created by X using Y data, with Z as the reasoning.”
👉When users understand where a decision came from, they’re more likely to trust the decision, and are also more equipped to challenge or refine it if necessary. PhotoG turns black-box outputs into auditable conversation units, which is a hallmark of transparent AI UX.
PhotoG_ Role Tagging for Each Output & Flexible Flow
Throughout the content generation process, PhotoG employs three types of feedback loops. The flow of trust in PhotoG begins with quick acknowledgement. When users set a goal, the system immediately responds with a confirmation, signaling that it has understood the request and is already preparing the next step. This small but instant feedback reduces hesitation and keeps the interaction moving smoothly.
The presentation of results, instead of producing a flat block of text, PhotoG generates structured industry reports complete with charts, growth metrics, and explanatory captions. Each chart is paired with contextual interpretation so users do not just see the numbers, they understand what those numbers mean. This layered presentation makes the output feel grounded, credible, and easy to act upon.
PhotoG- Result Presentation
Finally, the system allows users to refine the process without starting over. By switching roles or adjusting the direction of analysis, users can shape the output on the fly. This creates a sense of co-creation rather than one-sided automation, reinforcing both control and trust.
👉 Rather than locking users into a fixed AI pipeline, PhotoG treats the interface as a feedback-rich conversation. If the user disagrees with the brand direction analysis, they can immediately adjust the parameters, and the system re-runs the relevant steps using the updated logic.
The result is a closed input- adjust- feedback loop, in which:
Transparency shows the process,
Feedback validates the process,
Editability empowers the user to refine the process.
If you’re interested in applying this kind of transparent AI UX to your own product, the team at VSDesign is actively helping early-stage founders build trustworthy, user-centered AI interfaces:
🧭 Conclusion: Designing AI That Deserves Trust
Transparency in AI goes beyond convenience. It represents accountability and shared agency. When people can see which agent produced a decision, why a certain dataset was used, and how one step led to another, they are no longer passive recipients of machine output. They are active participants who can question, refine, or even reject a recommendation. This shift is especially important in professional and high-stakes settings such as strategy, finance, design, or hiring. In those contexts, users do not simply want quick answers. They need evidence they can trust and build upon.
Trust does not come automatically with accuracy or speed. It is not a reward that appears when a system reaches a certain performance level. Trust is created through design. It grows when an AI system makes its reasoning visible and when users are invited into that reasoning process.
The PhotoG platform illustrates how this can be achieved. It narrates its logic step by step, shows moments of active processing, attributes outputs to clear roles and sources, and allows users to edit the reasoning flow directly. The intention is not to overwhelm with technical details, but to create a shared cognitive space where human and machine thinking converge.
For AI startups and product teams, the message is clear. Show your users how the system thinks. Allow them to shape that thinking. Approach the design of the interaction with the same care that you put into the model itself. Transparency is not just a pathway to trust. It is the foundation of a relationship between people and the intelligent tools they choose to rely on.