AI transparency claims: what should buyers ask?
Last reviewed June 2, 2026
AI transparency claims say the product is explainable, auditable, traceable, disclosed, or clear about how AI is used. This guide maps those public wording patterns to the evidence a buyer should request before relying on the claim.
Evidence buyers verify
- The exact transparency, explainability, auditability, traceability, disclosure, or source-visibility claim.
- A product-surface map showing where the transparency claim applies and where it does not.
- Screenshots or copy showing user notice, generated-content labels, source references, confidence or limitation text, and handoff states.
Opens the checker for this claim type. Paste your vendor's exact wording there. Evidence questions only — not a blacklist or fraud detector. Not sure what a result looks like? See a sample receipt.
Sources this guide draws from
- NIST AI Risk Management Framework 1.0 NIST standard· January 26, 2023
Official framework source for transparency, accountability, measurement context, and trustworthy AI evidence questions.
- NIST AIRC AI risks and trustworthiness excerpt NIST standard· Excerpt from NIST AI RMF 1.0 (2023)
Source for transparent, accountable, privacy-enhanced, secure, and human-oversight evidence questions.
- UK ICO guidance on AI and data protection UK ICO guidance· Updated March 15, 2023; under review as of June 1, 2026
Regulator guidance source for AI transparency, lawfulness, fairness, security, data minimisation, and individual rights questions.
- EDPB automated decision-making and profiling guidelines EDPB guidance· Endorsed 2018; accessed June 1, 2026
Regulator guidance source for meaningful information, automated-decision safeguards, and human review path questions.
- EUR-Lex Regulation (EU) 2024/1689 European Parliament and Council standard· 13 June 2024
Official legal text source for AI system transparency obligations, user notice, generated-content marking, and role boundaries.
- Intercom Fin AI Agent FAQs Intercom company-page· Accessed May 24, 2026
Public company source for user-facing AI agent, chatbot role, answer-source, and capability wording; used as claim wording evidence, not independent validation.
- OpenAI business data page OpenAI company-page· Accessed June 1, 2026
Public company source for no-training-by-default, data ownership, confidentiality, and business-data boundary wording.
Public claims with documented evidence gaps
"Fin AI Agent is more than a chatbot"
Vague AI-powered- Source and date
- Intercom Fin AI Agent FAQs · Accessed May 24, 2026
- Evidence signal
- User-facing AI role wording without a visible disclosure point, source boundary, or handoff explanation in the claim itself.
- Evidence gap
- A buyer needs the user notice, role description, answer-source visibility, handoff trigger, and record of what the agent can and cannot do.
- Buyer question
- For the more than a chatbot claim, where does the product explain that the user is interacting with AI and what actions the AI can take?
"We don't train our models on your organization's data by default"
Compliance / Safety- Source and date
- OpenAI business data page · Accessed June 1, 2026
- Evidence signal
- Training-use transparency wording that depends on product surface, plan, opt-in settings, feedback, fine-tuning, and subprocessors.
- Evidence gap
- A buyer needs the product surfaces covered, opt-in conditions, data categories, retention period, subprocessor path, DPA language, and exclusions.
- Buyer question
- For the no-training-by-default claim, which prompts, files, outputs, feedback, API settings, and fine-tuning workflows are covered or excluded?
"EU hosted workspaces can use Fin AI Agent, with data processed securely within Europe"
Compliance / Safety- Source and date
- Intercom Fin AI Agent FAQs · Accessed June 1, 2026
- Evidence signal
- Regional-processing transparency wording without the full data-flow, log, support-access, and subprocessor boundary in the claim.
- Evidence gap
- A buyer needs which prompts, answers, source documents, logs, analytics, support events, and model-provider paths stay in region or leave it.
- Buyer question
- For the Europe-processing claim, which AI data types stay in Europe and which support, logging, or model-provider steps happen elsewhere?
Match each claim pattern to the evidence buyers need
| Claim pattern | Evidence needed | Buyer question |
|---|---|---|
| Transparent AI, explainable AI, or explainability claim | Explanation type, user-visible reason, input factors, model or rule boundary, confidence or uncertainty display, limitations, and human review path. | What can a user or reviewer actually see about why the AI produced this output? |
| Auditable AI, traceable AI, or source-traced output | Audit-log coverage, source links, model version, prompt or retrieval path, user action log, export format, retention period, and excluded events. | Can the buyer trace a specific output back to source material, model version, user action, and review record? |
| AI disclosure, AI-generated content label, or chatbot notice | Notice text, timing, placement, affected user, generated-content label, handoff path, and settings that cannot remove the disclosure. | Where does the user see that AI is involved before relying on the answer, content, or interaction? |
| Data-use transparency, no-training, or customer-data boundary claim | Product surfaces, data categories, opt-in settings, retention, support access, subprocessors, DPA terms, and exclusions. | Which data is used for training, evaluation, safety review, support, logging, or subprocessors despite the public wording? |
| Human review, appeal, or oversight transparency claim | Reviewer role, trigger conditions, approval point, appeal or correction path, override records, and outputs that bypass review. | Which AI outputs reach users without human review, and what record shows when a human reviewed or overrode the output? |
Evidence to request
- The exact transparency, explainability, auditability, traceability, disclosure, or source-visibility claim.
- A product-surface map showing where the transparency claim applies and where it does not.
- Screenshots or copy showing user notice, generated-content labels, source references, confidence or limitation text, and handoff states.
- Audit-log and traceability evidence covering model version, source retrieval, prompt or tool use, user action, reviewer action, and retention period.
- Data-use and human-review boundaries: training use, retention, subprocessors, support access, appeal path, and outputs that bypass review.
Questions to put in front of the vendor
- For this AI transparency claim, what exactly becomes visible to the user, admin, auditor, or buyer?
- Does the claim cover explanations for model output, data use, generated content, user notice, source traceability, or human review?
- Which parts of the AI workflow are logged, exportable, and retained long enough for review?
- Can a user or reviewer trace an output to source material, model version, prompt or tool path, and reviewer action?
- What transparency evidence changes by product plan, customer configuration, region, or connected app?
- What wording should replace the claim if only one workflow, output type, or admin surface is transparent?
Wording boundaries to compare against
- Shows source links and confidence notes for specified answer types; unsupported answers route to handoff.
- Provides audit logs for named admin events, connector access, and user actions for [retention period].
- Displays an AI interaction notice in specified chatbot workflows before the user relies on the answer.
- Customer data is not used for shared model training by default for named products and configurations; exclusions are documented separately.
Frequently asked questions
- What evidence supports an AI transparency claim?
- Ask what becomes visible, to whom, and at what workflow step. Useful evidence includes user notice text, source references, explanation fields, confidence or limitation notes, audit-log coverage, data-use boundaries, and the human review or appeal path.
- How is explainable AI different from traceable AI in vendor claims?
- Explainable AI wording should show what factors, sources, or rules influenced an output. Traceable AI wording should let a reviewer follow a specific output back to source material, model version, prompt or tool path, user action, and reviewer action. A vendor may support one without supporting the other.
- What should buyers ask about AI disclosure claims?
- Ask where the disclosure appears, who sees it, whether it is shown before the user relies on an AI answer or generated content, what configurations can remove it, and what happens when the AI hands off to a person.
Have your vendor's exact claim wording ready?
Check an AI transparency claim How the evidence method works