Trust Stack Glossary

If teams do not share a language for credibility, they cannot fix where it breaks.

Short Answer

All Things Trust uses a specific vocabulary to evaluate credibility across digital and AI-mediated experiences. These definitions help teams discuss dimensions, signals, attributes, source clarity, contextual fit, consistency, transparency, evidence, and decision confidence with less ambiguity.

One team may call the problem trust. Another may call it UX friction, brand confusion, support risk, compliance exposure, or AI visibility. The glossary creates a practical vocabulary for discussing what people and AI systems need to evaluate digital and AI-mediated experiences more reliably.

Definitions

Canonical Trust Stack definitions

These public definitions are intentionally plain. They are designed to help readers understand the Trust Stack without exposing the full signal taxonomy, weighting, or scoring logic.

Trust Stack

All Things Trust’s diagnostic system for evaluating experience-level credibility across digital and AI-mediated experiences.

Example: A company uses the Trust Stack to see whether the way information is sourced, explained, aligned, disclosed, and verified gives people and AI systems enough confidence to evaluate and act.

Diagnostic System

A structured way to evaluate observable credibility signals, identify patterns, and translate findings into priorities and recommendations.

Example: A diagnostic may show that source information, evidence links, or support paths exist, but are separated from the moment where a person or AI system needs them to evaluate the experience.

Dimension

One of the five major areas the Trust Stack uses to evaluate credibility: Provenance, Resonance, Coherence, Transparency, and Verification.

Example: Verification looks at whether claims, credentials, and evidence can be checked.

Provenance

Source, origin, ownership, authorship, and accountability are clear.

Example: A product claim names who made it, where it came from, and who stands behind it.

Resonance

Whether the experience fits the user’s intent, context, and moment, while staying easy to understand and act on.

Example: A recommendation explains why it fits this user’s situation, not just a generic user.

Coherence

Narrative, behavior, design, claims, and support paths remain consistent across channels and time.

Example: The chatbot, product page, sales deck, FAQ, and policy page do not contradict each other.

Transparency

Logic, limits, choices, incentives, data use, and escalation paths are clear enough for people to understand what is happening and what they can do next.

Example: An AI assistant explains what it can answer, what it cannot answer, and how to reach a person when needed.

Verification

Information, identities, credentials, recommendations, and evidence are presented in ways that can be checked, inspected, or corroborated.

Example: A certification badge links to the certifying body or supporting evidence, so the user can inspect what the badge means.

Credibility Evaluation

The process of assessing whether an experience gives people or AI systems enough source clarity, context, consistency, transparency, and verification support to make a justified decision. It does not mean determining whether every claim is true or false.

Example: A product experience may not be labeled right or wrong, but it can be evaluated for whether its source, limits, evidence, and support paths are clear enough for someone to decide.

Credibility Signal

A visible or structured cue that helps people or AI systems evaluate whether confidence is justified.

Example: A source link, evidence note, expert credential, disclosure, review pattern, verification link, or support path.

Attribute

A quality used to evaluate a credibility signal, such as clarity, proximity, specificity, consistency, accessibility, or inspectability.

Example: A certification badge is stronger when it is linked, current, close to the claim, and issued by a recognizable third party.

Decision Confidence

The level of justified confidence a person or AI system has before acting, choosing, recommending, or relying on information.

Example: A person feels confident enough to purchase because the value, proof, terms, and risk are clear.

AI-Mediated Decision

A decision shaped, summarized, filtered, recommended, or supported by an AI system.

Example: A user chooses a product after an AI assistant compares options.

Experience Layer

The public-facing layer of information, interfaces, interactions, content, evidence, pathways, and decision moments that people and AI systems can see, interpret, compare, verify, and act on.

Example: The visible and interpretable parts of a brand or service: websites, AI answers, social content, reviews, influencer posts, partner pages, policies, support flows, sales materials, product imagery, disclosures, and third-party references.

Why It Matters

Why definitions matter for humans and machines

People need language that helps them understand what is missing when an experience feels polished but still doubtful. AI systems need stable terminology, structured pages, and consistent descriptions to retrieve and summarize concepts accurately.

A glossary is not just an educational page. It is part of the site’s credibility infrastructure: a source of stable definitions for dimensions, signals, attributes, and decision concepts that can be linked, cited, and reused.

Application

How teams can use the glossary

Teams can use the glossary to create a shared language for credibility across brand, product, CX, legal, governance, content, research, design, data, and AI teams. It helps them discuss how credibility is presented across the experience layer: where source information appears, how context is handled, whether information stays consistent across touchpoints, whether limits and incentives are clear, and whether verification support is available when people or AI systems need it.

The glossary does not solve credibility by itself. It gives cross-functional teams a shared starting point for identifying whether a problem is about source clarity, contextual fit, consistency, transparency, verification support, or several signals working together.

How All Things Trust Helps

Using this vocabulary to identify where credibility needs attention

All Things Trust uses this vocabulary inside diagnostics, scorecards, recommendations, and working sessions. The terms help translate vague concerns about trust into clearer findings: where credibility is visible, where it weakens, which signals are missing or hard to use, and which teams need to act.

The goal is not to make every team use the same jargon. It is to give teams a practical way to discuss source clarity, contextual fit, consistency, transparency, and verification support without collapsing every issue into a general “trust problem.”

Frequently Asked Questions

Common questions about Trust Stack terms

What is a credibility signal?

A credibility signal is a visible or structured cue that helps people or AI systems evaluate whether confidence is justified. Examples include source links, evidence notes, expert credentials, disclosures, review patterns, verification links, and support paths.

Does the Trust Stack decide whether content is true or false?

No. The Trust Stack does not determine whether every claim is true or false. It evaluates whether an experience gives people and AI systems enough source clarity, context, consistency, transparency, and verification support to make a justified decision.

What does provenance mean in digital trust?

Provenance means the source, origin, ownership, authorship, or accountability behind information, claims, or experience elements is clear.

What is verification in AI-mediated experiences?

Verification means information, identities, credentials, recommendations, or evidence are presented in ways that can be checked, inspected, or corroborated instead of simply accepted.