Vignesh Hari Krishnan / v0.1
Trust Signals in AI
A design framework for AI trust
Trust in AI is a design problem, not just an ethics or engineering one. The moments where users lose confidence in an AI system, feel surveilled by it, or stop believing what it tells them: these are interaction failures. Not ethics failures. Not engineering failures.
They happen at the surface, in the reading experience, in the half-second before a user decides whether to act on what they just read.
This library names those moments, maps what causes them, and proposes design responses.
A user who receives a confident, fluent, beautifully formatted hallucination does not think "this model is miscalibrated." They think "I was misled."
Why Design
When something is easy to read, it feels true. This is not a flaw in human cognition; it is an efficient heuristic called the fluency effect. AI breaks it because fluency is completely decoupled from accuracy.
A hallucinated claim arrives in the same grammatical, well-structured prose as a verified fact. The interface offers no friction to slow the reader down.
AI products have inherited UI conventions from software that never had to lie. The interface was not designed for a system that can be confidently wrong.
Engineering can improve a model's calibration: how often its expressed confidence matches its actual accuracy. But calibration is a property of the model, invisible to users until it fails.
Design can make calibration perceptible. It can surface the gap between what an AI knows and what it is claiming to know, at the exact moment the user is deciding whether to trust it.
Trust is not established once, at onboarding. It is rebuilt or eroded with every interaction. A system that handles failure gracefully, that makes its errors legible, its corrections visible, its limits honest, can be trusted more than a system that performs perfection.
Framework
Existing AI trust frameworks organize around properties: transparency, fairness, control. They describe what trust is in a system.
This framework organizes around what trust runs onin a user's experience: four axes, each a question the user is asking implicitly in every interaction.
The fourth axis — relation — has almost nothing in existing design work. AI is the first software that behaves like an agent: it responds, adapts, has opinions, and produces outputs that feel authored.
Trust States
Trust is not binary. In every interaction with an AI system, a user is in one of three states, and the appropriate design response differs substantially.
Forming
First encounter. The user is building a mental model. Everything the system does is calibration data. Trust is being established from zero.
Stressed
Something unexpected happened. A pattern fired. The user's mental model is being challenged or contradicted.
Repairing
The system is recovering from a failure. The user is deciding whether to extend trust again, on what terms and to what degree.
Most AI design focuses entirely on the forming state. The stressed and repairing states are almost entirely undesigned. Research on service recovery shows consistently that how a failure is handled matters more to long-term trust than whether the failure occurred at all.
Legibility Failures
The user cannot tell what the AI knows, does not know, or is doing. The interface presents all AI outputs with uniform confidence, regardless of what the model actually knows.
The AI sounds equally certain about everything it says
Where did that answer come from?
What can this thing actually do?
How did you get there?
Agency Erosion
The user loses the feeling of being in control. The system treats user input as a trigger to respond to rather than a communication to understand.
A hard stop with no path forward
Same intent, different wording, unpredictable output
Did it actually understand my correction?
The system made a choice I did not know about
Consistency Breaks
The system behaves unpredictably across time, context, or surface. What worked yesterday does not work today.
It forgot what we were talking about
Why is it suddenly talking like that?
This used to work differently
That word means something different now
Relational Friction
The interaction feels inauthentic, instrumental, or misaligned. The system behaves in ways that would be inappropriate or manipulative between humans.
It agrees with everything I say
The hedging feels fake
It remembered something I did not want it to
It is being too familiar
Signal + Channel
Patterns are combinations of what the system needs to communicate (the signal) and where or how it communicates it (the channel). The same signal through a different channel produces a different pattern.
Combination Matrix
| Signal ↓ / Channel → | weight/opacity | reveal gesture | streaming tempo | inline edit | diff/delta | ambient notif. | pre-response |
|---|---|---|---|---|---|---|---|
| certainty | ◆ | ◆ | ↗ | · | · | · | · |
| drift | · | ◆ | · | · | ↗ | · | · |
| intent model | · | · | · | ◆ | · | · | ◆ |
| provenance | · | ↗ | — | · | · | · | · |
| revision history | · | ◆ | — | · | ◆ | · | — |
| mode | ◆ | · | ↗ | · | — | · | ◆ |
| attention | · | ↗ | — | · | · | · | ↗ |
| persistence | · | ◆ | — | ↗ | — | ◆ | — |
Signals
These are the information types the system needs to communicate to build trust.
Channels
These are the surfaces or mechanisms through which signals are communicated.