Trust Stack Glossary

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

Direct Answer

All Things Trust uses a shared vocabulary to evaluate credibility across digital and AI-mediated experiences. These definitions help teams discuss credibility with less ambiguity: where it is strong, where it breaks down, and what needs to be clarified, corrected, connected, or strengthened.

One team may call the problem trust. Another may call it UX friction, brand confusion, support risk, compliance exposure, or AI visibility. The glossary gives teams a shared way to discuss the same underlying issue: whether people and AI systems have enough clear, consistent, and supported information to evaluate an experience with confidence.

Definitions

Core Trust Stack definitions

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

Trust Stack

All Things Trust’s diagnostic system for evaluating whether public-facing digital and AI-mediated experiences are clear, consistent, supported, and credible enough for people and AI systems to evaluate, rely on, and act on.

Plain example: A company uses the Trust Stack to see whether the claims, content, recommendations, reviews, creator partnerships, disclosures, support paths, and evidence around its products or services are clear and supported enough for people to evaluate with confidence, and for AI systems to interpret accurately.

Diagnostic system

A structured way to examine observable credibility signals, identify where they hold or break down, and translate findings into priorities and recommendations.

Plain example: A diagnostic may show that source information, evidence links, disclosures, or support paths exist, but are separated from the moments where people or AI systems need 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.

Plain example: A team may find that a credibility issue is not one vague trust problem, but a Provenance, Resonance, Coherence, Transparency, or Verification weakness that can be examined more specifically.

Provenance

Whether the source, origin, ownership, authorship, and accountability behind information, claims, content, recommendations, partnerships, and product representations are clear.

Plain example: A person sees a product claim in an ad, a creator post, a comparison page, and an AI summary. Provenance looks at whether the source, ownership, sponsorship, authorship, and accountability behind those claims are clear across the experience.

Resonance

Whether the experience fits the user’s intent, context, and moment, while remaining clear enough to understand and act on.

Plain example: A person comparing products sees guidance that reflects their actual need, context, constraints, and stage of decision, rather than a generic recommendation that could apply to anyone.

Coherence

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

Plain example: The chatbot, product page, creator brief, FAQ, policy page, and support flow do not contradict one another about what the product does, what is promised, and what happens if something goes wrong.

Transparency

Whether 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.

Plain example: An AI assistant or product experience makes clear what information is being used, what limits apply, whether incentives or partnerships are involved, and how a person can get help or escalate when needed.

Verification

Whether product claims, credentials, reviews, recommendations, evidence, and performance promises are presented in ways that can be checked, inspected, or corroborated.

Plain example: A shopper sees a product claim on a website, hears it repeated by an influencer, finds it in reviews, and sees it summarized by AI. Verification looks at whether the evidence behind that claim is specific, accessible, and consistent enough to check across those moments.

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. A credibility evaluation does not decide whether every claim is true or false. It examines whether the experience provides enough support for someone to evaluate the claim responsibly.

Plain example: A product experience may not be labeled right or wrong, but it can be evaluated for whether its sources, limits, evidence, claims, disclosures, 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.

Plain example: A source link, evidence note, expert credential, disclosure, review pattern, verification link, certification, support path, or third-party reference.

Attribute

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

Plain 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 has, or the level of interpretive support an AI system has, before acting, choosing, recommending, or relying on information.

Plain example: A person feels confident enough to purchase because the value, proof, terms, limits, and risks are clear.

AI-mediated decision

A decision influenced by information an AI system summarizes, filters, recommends, compares, or helps evaluate.

Plain example: A person chooses a product, service, provider, or location after an AI assistant compares options and summarizes what appears most relevant.

Experience layer

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

Plain example: The visible and interpretable parts of a brand, product, 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 people and AI systems

People need language that helps them name 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 stable source of 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 practical starting point for identifying whether a problem is about source clarity, contextual fit, consistency, transparency, verification support, or several signals working together.

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, certifications, support paths, and third-party references.

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 provides enough source clarity, context, consistency, transparency, and verification support for people or AI systems to evaluate claims responsibly.

What does provenance mean in digital trust?

Provenance means the source, origin, ownership, authorship, or accountability behind information, claims, recommendations, content, partnerships, or experiences.

What is verification in AI-mediated experiences?

Verification means product claims, credentials, reviews, recommendations, evidence, and performance promises are presented in ways that can be checked, inspected, or corroborated.

Why does a shared vocabulary matter?

Because credibility problems often get misnamed. One team may call something a UX issue, another may call it a brand issue, and another may call it a compliance issue. A shared vocabulary helps teams identify the actual weakness and decide what to fix.

How All Things Trust Helps

From shared language to clearer decisions

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.”

Use the Trust Stack to identify where credibility is strong, where it breaks down, and what needs to be clarified, corrected, connected, or strengthened.

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