# EVIDENCE_STANDARDS.md # Standards Body Evidence Standards **Project:** Standards Body **Primary domain:** standardsbody.ai **Core line:** Foundations for Frontier AI **Document type:** Canonical evidence-quality, claims, confidence, sourcing, and decision standard **Version:** 1.0 **Status:** Approved foundational source **Document owner:** Standards Body **Applies to:** All Standards Body research, foundation papers, evaluation protocols, review findings, standards proposals, institutional claims, public reports, website copy, case studies, source registries, partnerships, contributor work, and future assurance activities **Related canonical sources:** `PROJECT_IDENTITY.md`, `TERMINOLOGY.md`, `FOUNDATIONS_APPENDIX.md`, `RESEARCH_METHODOLOGY.md`, `EVALUATION_PHILOSOPHY.md`, `TRANSPARENCY_FRAMEWORK.md`, `VERSION_HISTORY.md` **Research basis reviewed through:** July 16, 2026 **Review cycle:** Annual review, with event-triggered revision after a material change in evaluation science, measurement standards, conformity assessment, legal requirements, research methods, project authority, or public-claims practice --- ## Document Purpose This document defines the evidence standards of Standards Body. It governs: - What counts as evidence - Which evidence is appropriate for different claims - How evidence quality should be assessed - How primary and secondary sources should be used - How technical, organizational, legal, policy, and institutional claims differ - How uncertainty and confidence should be expressed - How contradictory evidence should be handled - How confidential evidence may support public conclusions - How model-generated analysis may and may not be used - How causal, comparative, predictive, negative, and absence claims should be supported - How evidence should be documented, cited, versioned, corrected, withdrawn, and retired - What level of evidence is required before Standards Body adopts a position, publishes a claim, proposes a standard, supports a threshold, or participates in an assurance activity - How evidence packages should be structured for review, standards development, and decision-making This file is the canonical source for evidentiary sufficiency within Standards Body. It does not replace domain expertise. It does not create one universal hierarchy in which every randomized study is automatically stronger than every operational record, every public source is stronger than every confidential source, or every quantitative result is stronger than every expert judgment. Evidence quality depends on: - The claim - the decision - the evaluated object - the method - the context - the consequence of error - the source's access - the source's independence - the age of the evidence - the ability to verify or challenge it The central requirement is not maximum volume of evidence. It is evidence fit for the claim and decision. --- # Executive Summary Frontier AI institutions regularly make claims that exceed their evidence. Examples include: - A benchmark result is presented as proof of general capability. - A failed elicitation attempt is presented as proof that a capability is absent. - An internal review is presented as an independent audit. - A management-system certificate is presented as proof that a model is safe. - A policy proposal is presented as though technical evidence uniquely determines it. - An expert's prediction is presented as established fact. - A confidential finding is invoked without explaining who reviewed it or what the public can reasonably infer. - A model-generated summary is cited as an independent source. - A legal requirement is described from memory rather than the authoritative legal text. - A historical result is applied to a model or system that has materially changed. - A public claim omits contrary evidence, uncertainty, or known limitations. Standards Body rejects these practices. The project adopts the following core proposition: > **A claim should be no broader, more certain, more current, or more authoritative than the evidence supporting it.** Evidence standards should connect four elements: 1. The claim being made 2. The evidence supporting or challenging it 3. The confidence justified by that evidence 4. The decision or public statement the evidence is being used to support A technically impressive result can still be weak evidence when: - The construct is unclear - the system identity is incomplete - the tasks are contaminated - the administration conditions are unrealistic - the result cannot be reproduced - the evaluator lacks independence - the sample is unrepresentative - the evidence is stale - the conclusion goes beyond the tested conditions A qualitative record can be strong evidence when: - The claim is institutional or procedural - the source had direct access - the record has reliable provenance - multiple independent accounts corroborate it - the limitations are visible - the evidence is decision-relevant Standards Body therefore evaluates evidence across multiple dimensions rather than using a single source hierarchy. The core dimensions are: - Relevance - directness - validity - reliability - provenance - completeness - independence - recency - representativeness - reproducibility - security and integrity - uncertainty - decision relevance - consistency with other evidence - susceptibility to manipulation The same evidence may receive different weight for different claims. A developer's internal system log may be strong direct evidence that an event occurred. It may be weak evidence for the claim that the organization's controls are effective across all deployments. An external evaluator's report may be strong evidence for performance under one protocol. It may be weak evidence for broad legal compliance. A certification may be strong evidence that specified requirements were assessed under a defined scheme. It is not automatic evidence that a model is safe, unbiased, secure, or suitable for every use. The Standards Body evidence model contains five evidence levels. ## Level E0: Unsupported The claim rests primarily on assertion, reputation, intuition, marketing, or unverifiable information. ## Level E1: Preliminary Some relevant evidence exists, but it is limited, indirect, weakly controlled, unreplicated, or materially incomplete. ## Level E2: Supported Multiple relevant sources or one strong direct source support a bounded claim, with important limitations remaining. ## Level E3: Substantiated The claim is supported by strong, traceable, appropriately designed evidence, independent challenge, uncertainty analysis, and material contrary-evidence review. ## Level E4: Decision-Grade The evidence is sufficiently mature, current, secure, independently reviewable, and decision-linked for a specified consequential use. These levels apply to a defined claim, not to a source or organization in the abstract. Evidence level is not the same as confidence. Confidence also depends on: - Agreement among sources - unresolved uncertainty - model or system change - hidden information - potential bias - decision sensitivity Standards Body uses five confidence labels: - Very low - low - moderate - high - very high Very high confidence should be rare in frontier AI work. A strong evidence package should include: - The exact claim - system identity - protocol or method - source register - direct and supporting evidence - contrary evidence - uncertainty - conflict information - security status - reviewer findings - confidence - claim limitations - expiration or review date - correction history The project should prefer primary sources for: - Legal claims - standards claims - institutional status - evaluation results - company policies - technical methods - public commitments - regulatory decisions Secondary sources remain valuable for: - Synthesis - context - criticism - comparison - discovery - interpretation They should not displace authoritative primary material when that material is available. Confidential evidence may be necessary for: - Model access - protected task results - security incidents - proprietary system details - personal data - national-security information A public conclusion based partly on confidential evidence must state: - That nonpublic evidence was used - who had access - what type of review occurred - what limitations the confidentiality creates - what public claim remains justified - whether an independent reviewer corroborated the evidence Model-generated text may assist: - Search planning - source discovery - extraction - comparison - drafting - consistency review It may not serve as independent evidence. The human or institution publishing the claim remains responsible for verifying every substantive statement. The final rule is: > **Show the claim, show the evidence, show the limitations, show the confidence, and show who is responsible.** --- # 1. Foundational Propositions ## 1.1 Claim-Bounded Evidence > **Evidence should be assessed against a specific claim, not judged as strong or weak in the abstract.** ## 1.2 Decision-Bounded Sufficiency > **The amount and quality of evidence required should rise with the consequence, irreversibility, and uncertainty of the decision.** ## 1.3 Source-Proximity Principle > **Where available, the source closest to the relevant event, system, method, law, or decision should normally anchor the evidence record.** ## 1.4 Multiple-Dimension Principle > **Evidence quality should be assessed across several dimensions rather than reduced to one hierarchy or score.** ## 1.5 Contrary-Evidence Principle > **A credible evidence review actively searches for evidence that could weaken or overturn the preferred conclusion.** ## 1.6 Traceability Principle > **Every consequential conclusion should be traceable to identifiable sources, methods, assumptions, reviewers, and versions.** ## 1.7 Uncertainty Principle > **Uncertainty should be treated as part of the evidence output rather than removed to create a cleaner conclusion.** ## 1.8 Recency Principle > **Evidence should be re-evaluated when the model, system, protocol, deployment, threat, standard, law, or institution materially changes.** ## 1.9 Independence Principle > **Evidence produced by an interested party may be important and direct, but its weight should account for incentives, access, conflicts, and external challenge.** ## 1.10 Reproducibility Principle > **Evidence becomes more credible when qualified others can reproduce, replicate, reperform, or otherwise independently test the relevant claim.** ## 1.11 Confidentiality Principle > **Evidence does not become invalid merely because it is confidential, but confidentiality increases the burden for governance, independent review, traceability, and claim limitation.** ## 1.12 No Evidence Laundering > **A citation, expert title, certification, institutional logo, or model-generated summary should not be used to make weak evidence appear stronger than it is.** ## 1.13 Correction Principle > **The credibility of an evidence institution depends partly on how visibly and effectively it corrects error.** ## 1.14 Authority Principle > **Technical evidence can inform authority, but evidence does not create legal or regulatory authority by itself.** --- # 2. Scope ## 2.1 Included Evidence Domains This standard applies to: - AI evaluation results - benchmark and protocol claims - capability and safeguard findings - security evidence - incident evidence - organizational and governance evidence - audit and certification evidence - accreditation and competence evidence - legal and regulatory claims - standards and conformity claims - policy analysis - forecasts - expert judgments - public statements - historical records - case studies - international comparisons - research synthesis - website and media claims ## 2.2 Excluded Functions This standard does not: - Replace a court's evidentiary rules - replace professional legal standards - establish medical or clinical evidence rules - establish national-security classification - guarantee scientific truth - eliminate expert disagreement - require public disclosure of all evidence - create certification or accreditation authority - determine public policy automatically ## 2.3 Higher Domain Standards Where a specialized field has stronger applicable evidence requirements, the stronger requirements should govern. Examples may include: - Clinical research - nuclear safety - aviation - laboratory metrology - cybersecurity incident response - classified national-security analysis - financial auditing - legal proceedings ## 2.4 Minimum Standard This document is a floor for Standards Body work. A project may adopt stricter evidence requirements. --- # 3. Canonical Evidence Definitions Definitions in `TERMINOLOGY.md` govern. The following definitions are central to this document. ## 3.1 Evidence Information, observation, artifact, record, result, or testimony relevant to supporting or challenging a claim. ## 3.2 Claim A proposition asserted to be true, supported, justified, or sufficiently reliable for a specified purpose. ## 3.3 Evidence Standard The level, type, quality, and sufficiency of evidence required for a defined claim or decision. ## 3.4 Evidence Package A structured set of artifacts supporting or challenging a claim, review, standard, certification, recognition, or decision. ## 3.5 Direct Evidence Evidence derived directly from the object, event, system, or process at issue. ## 3.6 Indirect Evidence Evidence supporting a claim through inference rather than direct observation. ## 3.7 Primary Source An original source of data, law, standards text, evaluation results, institutional policy, or first-hand record. ## 3.8 Secondary Source A source interpreting, summarizing, comparing, or analyzing primary material. ## 3.9 Tertiary Source A source that aggregates or simplifies primary and secondary material, such as a general encyclopedia, directory, overview, or automatically generated summary. ## 3.10 Corroboration Independent or meaningfully distinct evidence supporting the same claim. ## 3.11 Triangulation Use of multiple methods, sources, or perspectives to assess a claim. ## 3.12 Provenance The origin, history, custody, modification, and ownership of evidence or an artifact. ## 3.13 Chain of Custody A documented record of possession, transfer, access, and handling. ## 3.14 Traceability The ability to connect a claim, result, decision, or artifact to its sources, methods, versions, and responsible parties. ## 3.15 Reproducibility The ability to obtain consistent computational or analytical results using the same data, code, methods, and conditions. ## 3.16 Replicability The ability of an independent study or implementation to support a finding using new data, tasks, systems, or materially independent execution. ## 3.17 Reperformance Independent execution of a specified procedure to verify reported work. ## 3.18 Confidence The degree of justified belief in a conclusion based on available evidence. ## 3.19 Evidence Gap Missing evidence necessary to support, challenge, or resolve a claim. ## 3.20 Material Evidence Evidence that could reasonably change the conclusion, confidence, scope, or decision. ## 3.21 Contrary Evidence Evidence that weakens, contradicts, narrows, or creates material uncertainty about a claim. ## 3.22 Decision-Grade Evidence Evidence sufficiently mature, current, relevant, independently challengeable, and well-governed for a specified consequential decision. ## 3.23 Epistemic Status A concise statement describing how strongly a claim is supported and what uncertainty remains. --- # 4. The Evidence Object Standards Body treats an evidence object as more than a document or result. A complete evidence object includes: ## 4.1 Identifier A unique record or citation identifier. ## 4.2 Claim Relationship Which claim the evidence supports, challenges, or contextualizes. ## 4.3 Source Who or what produced it. ## 4.4 Date When the evidence was created, observed, published, or last verified. ## 4.5 Method How it was produced. ## 4.6 Evaluated Object The exact model, system, organization, process, law, standard, event, or decision addressed. ## 4.7 Scope The boundaries within which the evidence is relevant. ## 4.8 Result The observation, finding, record, or conclusion. ## 4.9 Uncertainty Known measurement, sampling, model, interpretive, or access limitations. ## 4.10 Provenance Origin, modification history, custody, and authenticity. ## 4.11 Independence Relevant relationships between source and claim. ## 4.12 Security Status Whether the evidence is public, controlled, confidential, restricted, or highly restricted. ## 4.13 Version and Status Whether the evidence is current, corrected, superseded, withdrawn, or expired. ## 4.14 Review Status Whether it received: - Internal review - peer review - independent expert review - audit - validation - verification - no formal review ## 4.15 Citation or Retrieval Location Where the evidence can be found or how authorized reviewers may access it. --- # 5. Claims Architecture Evidence standards begin with a correctly formed claim. ## 5.1 Claim Components A complete claim should identify: - Subject - asserted property or event - scope - time - conditions - comparison where relevant - degree of certainty - intended use ## 5.2 Weak Claim > Model X is safe. Problems: - Undefined system - undefined harm - undefined context - undefined period - undefined risk tolerance - no evidence boundary ## 5.3 Bounded Claim > Under Protocol Y version 1.3, System X version 4.2 did not exceed the defined critical cyber capability threshold in the assessed environment, using the stated tool access and elicitation budget, as of July 2026. This claim remains limited. It does not establish: - Absence of all cyber capability - universal safety - future performance - performance under other scaffolds - legal compliance - deployment suitability ## 5.4 Claim Decomposition Broad claims should be decomposed into testable subclaims. Example: "The evaluator is independent" may contain: - No ownership relationship - no result-dependent fee - no publication veto - controlled client concentration - method freedom - conflict disclosure - sufficient access - governance independence ## 5.5 Claim Owner Every material claim should have an identifiable owner responsible for: - Evidence selection - wording - limitations - correction - status ## 5.6 Claim Register Major projects should maintain a claim register containing: - Claim ID - wording - claim type - owner - evidence level - confidence - sources - contrary evidence - review date - publication location - status --- # 6. Evidence Levels Evidence levels express the maturity of support for one defined claim. They do not rank people, institutions, or publications. ## 6.1 Level E0: Unsupported Characteristics: - Assertion without traceable evidence - marketing language - anonymous or unverifiable report - reputation alone - speculative statement presented as fact - model-generated output without source verification - citation that does not support the claim Permitted uses: - Hypothesis generation - early signal logging - question formation Not permitted for: - Canonical factual claims - standards requirements - high-stakes thresholds - public assurance claims - certification decisions ## 6.2 Level E1: Preliminary Characteristics: - One limited source - indirect evidence - uncontrolled observation - unpublished internal result - small or unrepresentative sample - weak provenance - limited access - high uncertainty - no independent challenge Permitted uses: - Exploratory research - early warning - pilot design - provisional hypothesis - preliminary internal decision with caution Required language: - Preliminary - suggestive - early evidence - not independently verified - subject to material uncertainty ## 6.3 Level E2: Supported Characteristics: - One strong direct source or several relevant sources - defined method - identifiable object and version - reasonable provenance - bounded conclusion - uncertainty described - material limitations remain - some corroboration or review Permitted uses: - Working paper conclusions - bounded public claims - pilot decisions - proposed framework elements - risk-management action that remains reversible ## 6.4 Level E3: Substantiated Characteristics: - Strong direct evidence - appropriate design and controls - reproducible or independently challenged work - system and protocol identity - contrary-evidence review - uncertainty analysis - traceable provenance - material conflicts addressed - current evidence - clear limitations Permitted uses: - Canonical project positions - mature technical conclusions - proposed standards provisions - consequential internal decisions - public findings with bounded wording ## 6.5 Level E4: Decision-Grade Characteristics: - E3 qualities - explicit decision link - consequence-sensitive evidence design - strong independent review - adequate security and chain of custody - decision-relevant thresholds or criteria - current evaluator competence - documented false-positive and false-negative implications - correction and appeal process - expiration or monitoring - implementation evidence where relevant Permitted uses: - High-stakes deployment decisions - certification or conformity decisions under a valid scheme - procurement requirements - formal standards progression - recognition decisions - regulatory support where the authority accepts the evidence ## 6.6 No Automatic Promotion Evidence should not be promoted to a higher level because: - The source is famous - the organization is large - the result is quantitative - the paper is peer reviewed - the claim is urgent - many secondary sources repeat it - the evidence is confidential - the evidence is public - the source is a government - the source is a frontier laboratory ## 6.7 Evidence-Level Record Every E2 through E4 claim should record: - Assigned level - rationale - reviewer - date - unresolved gaps - conditions for promotion - expiration or reassessment trigger --- # 7. Evidence Quality Dimensions Evidence should be assessed dimension by dimension. A source may be strong on one dimension and weak on another. ## 7.1 Relevance Question: Does the evidence address the actual claim? Weak relevance examples: - Using a broad benchmark to support a narrow real-world capability claim - using management-system documentation to support a technical safety claim - using policy popularity to support technical validity - using one legal jurisdiction to describe another ## 7.2 Directness Question: How many inferential steps separate the evidence from the claim? Direct evidence generally deserves more weight, but indirect evidence can be essential when direct observation is impossible or unsafe. ## 7.3 Construct Validity Question: Does the evidence measure or represent the intended concept? Required considerations: - Construct definition - task coverage - proxy quality - alternative explanations - expert review - deployment relevance ## 7.4 Internal Validity Question: Does the design support the claimed explanation, comparison, or causal interpretation? Consider: - Controls - confounding - selection - leakage - administration consistency - scoring - missing data - analyst degrees of freedom ## 7.5 External Validity Question: Can the conclusion reasonably generalize beyond the assessed sample, environment, system, or period? ## 7.6 Reliability Question: Would repeated measurement under equivalent conditions produce materially consistent results? Consider: - Run-to-run variation - task sampling - judge consistency - model nondeterminism - environment stability - human-rater agreement ## 7.7 Provenance Question: Can the origin and history of the evidence be established? Strong provenance includes: - Original source - creation date - authorship - modification history - version - custody - signatures or hashes where relevant ## 7.8 Authenticity Question: Is the evidence genuine and unaltered? Potential controls: - Digital signatures - cryptographic hashes - trusted repositories - source confirmation - preserved originals - tamper-evident logs ## 7.9 Completeness Question: Does the evidence package include the material information needed for interpretation? Missing information may include: - Failed runs - excluded tasks - model configuration - elicitation budget - scoring changes - contrary findings - incidents - conflicts ## 7.10 Independence Question: What interests or relationships could influence production or interpretation? Independence affects weight, not automatic admissibility. Developer evidence may be uniquely informative. It should be identified as first-party evidence and independently challenged where the claim is consequential. ## 7.11 Recency Question: Is the evidence still applicable? Recency depends on: - Model change - system change - protocol change - threat change - law or standard change - institutional status - deployment context ## 7.12 Representativeness Question: Does the sample represent the relevant task universe, users, environments, languages, sectors, or threat actors? ## 7.13 Reproducibility Question: Can qualified reviewers recompute or re-execute the work using the same artifacts? ## 7.14 Replicability Question: Can materially independent evidence support the same finding? ## 7.15 Security and Integrity Question: Was the evidence protected against: - Leakage - contamination - tampering - unauthorized access - selective deletion - score manipulation ## 7.16 Measurement Uncertainty Question: Are uncertainty sources identified and, where feasible, quantified? ## 7.17 Interpretive Uncertainty Question: Could qualified reviewers reasonably interpret the same evidence differently? ## 7.18 Decision Relevance Question: Does the evidence reduce uncertainty for the actual decision? ## 7.19 Timeliness Question: Was evidence produced early enough to affect the decision? ## 7.20 Susceptibility to Gaming Question: Could the producer or evaluated party optimize the evidence without improving the underlying property? ## 7.21 Conflict Exposure Question: What financial, organizational, political, professional, or reputational conflicts affect the evidence? ## 7.22 Transparency and Reviewability Question: Can qualified reviewers understand and challenge the evidence? Full public disclosure is not always required. Sufficient reviewability is. ## 7.23 Legal and Ethical Fitness Question: Was the evidence collected, processed, and disclosed lawfully and ethically? ## 7.24 Burden and Proportionality Question: Was the evidence burden appropriate to the claim and consequence? Excessive evidence requirements can: - Delay beneficial action - exclude smaller actors - centralize authority - create compliance theater - consume scarce evaluator capacity ## 7.25 Overall Assessment Overall quality should be expressed as a reasoned profile. Do not average all dimensions into one score when a critical weakness is dispositive. --- # 8. Source Hierarchy and Source Use Standards Body uses a claim-specific source hierarchy. ## 8.1 Tier S1: Authoritative Primary Sources Examples: - Enacted law - official regulation - court decision - official standards text - regulator decision - original evaluation report - original dataset or artifact - signed institutional policy - direct system logs - original public filing - official registry record Use: Anchor claims concerning legal status, institutional status, standards content, evaluation methods, and direct events. Limitations: An authoritative source may still be incomplete, self-interested, outdated, or narrow. ## 8.2 Tier S2: Primary Research and Direct Technical Evidence Examples: - Peer-reviewed or openly reviewed research paper - preprint with complete methods - evaluation dataset - experiment record - independent technical report - incident investigation - reproducible code - expert review with direct access Use: Support technical, scientific, and evaluative claims. Limitations: Publication venue does not eliminate design, conflict, or reproducibility problems. ## 8.3 Tier S3: High-Quality Institutional Synthesis Examples: - National academy report - systematic review - standards-body explanatory guidance - government technical synthesis - international-organization analysis - transparent multi-expert report Use: Context, synthesis, comparison, and interpretation. Limitations: May lag current systems or reflect institutional consensus rather than direct evidence. ## 8.4 Tier S4: Credible Secondary Analysis Examples: - Academic commentary - specialist journalism - expert blog with sources - think-tank report - legal analysis - technical explainer Use: Discovery, criticism, context, and alternate interpretations. Limitations: Should not replace available primary evidence for consequential claims. ## 8.5 Tier S5: Preliminary or Informal Sources Examples: - Conference talk - interview - social-media statement - forum discussion - anonymous report - unverified leak - preliminary slide - personal communication Use: Early signals, hypothesis generation, source discovery. Limitations: Require corroboration before factual adoption. ## 8.6 Tier S6: Tertiary and Generated Sources Examples: - General encyclopedia - unsourced aggregator - search snippet - model-generated summary - automatically generated profile - citation index without source inspection Use: Navigation and discovery only. Not acceptable as sole support for a material claim. ## 8.7 Primary-Source Rule For legal, standards, product-policy, certification, accreditation, and institutional-status claims, inspect the authoritative primary source where available. ## 8.8 Secondary-Source Rule Use secondary analysis to: - Identify disagreement - expose limitations - explain context - compare institutions - locate primary evidence ## 8.9 Search-Result Rule Search-result snippets are not evidence. The underlying source should be opened and reviewed. ## 8.10 Citation-Count Rule Citation count does not establish validity. It may indicate influence or attention. ## 8.11 Peer-Review Rule Peer review is relevant evidence of review. It is not a guarantee of: - Correctness - replicability - current applicability - absence of conflict - policy relevance ## 8.12 Government-Source Rule Government sources may be authoritative for: - Their own rules - official decisions - official data - institutional position They are not automatically the strongest source for every technical or normative claim. ## 8.13 Company-Source Rule Company sources may be authoritative for: - Product releases - public policies - internal commitments - declared methods - first-party findings They require external challenge for consequential claims about effectiveness, safety, independence, or broad impact. ## 8.14 Anonymous-Source Rule Anonymous evidence should be used only when: - The information is material - identification would create credible risk - the recipient can assess access and credibility - corroboration exists where feasible - the public claim is limited ## 8.15 Personal-Communication Rule Record: - Speaker - date - role - context - permission - whether independently corroborated Avoid relying on unverifiable personal communication for canonical factual claims. --- # 9. Claim Types and Required Evidence Different claim types require different evidence. ## 9.1 Descriptive Claims Example: > The organization published a new evaluation framework on a specified date. Minimum evidence: - Primary publication - date - identity - current status ## 9.2 Technical Performance Claims Example: > System X achieved a specified success rate under Protocol Y. Minimum evidence: - System identity - protocol version - task population - administration - scoring - uncertainty - raw or reviewable evidence - date High-stakes addition: - Held-out integrity - independent review - replication or reperformance - contrary evidence - expiration ## 9.3 Capability Claims Example: > System X demonstrated autonomous performance in a defined capability domain. Minimum evidence: - Construct - task family - elicitation - system configuration - success criteria - reliability - autonomy - resource use - limitations Avoid: - Generalizing from knowledge questions to operational capability - generalizing from one scaffold to every deployment ## 9.4 Absence Claims Example: > The system does not possess Capability C. This is a strong claim. Required evidence may include: - Broad and valid task coverage - strong elicitation - multiple methods - sufficient power - repeated negative results - external challenge - defined upper bounds - explicit scope Preferred wording when evidence is weaker: > The capability was not demonstrated under the assessed conditions. ## 9.5 Safety Claims Example: > The system is safe. This claim is normally too broad. Required replacement: - Define harm - context - risk tolerance - safeguards - evidence - time - residual risk Preferred form: > The system met the specified safeguard and risk requirements under the assessed deployment conditions. ## 9.6 Safeguard-Effectiveness Claims Minimum evidence: - Threat model - baseline - attack methods - adaptive testing - bypasses - operational conditions - residual risk - uncertainty - independent challenge ## 9.7 Security Claims Minimum evidence: - Asset - threat - control - attack surface - test scope - vulnerability status - incident history - time Avoid: - Secure - unhackable - failsafe without defined bounds. ## 9.8 Reliability Claims Minimum evidence: - Task distribution - repeated trials - environment variation - failure definition - missing or invalid runs - uncertainty - distribution-shift analysis ## 9.9 Comparative Claims Example: > System A is more capable than System B. Minimum evidence: - Comparable system definitions - same or mapped protocol - same administration - equivalent resources - uncertainty - statistical or qualitative comparison - material configuration differences ## 9.10 Superiority Claims Example: > Method A is the best available evaluation method. Required evidence: - Defined alternatives - comparison criteria - multiple settings - limitations - independent replication - recency Preferred wording when incomplete: > Method A showed stronger performance on the defined criteria in the assessed settings. ## 9.11 Causal Claims Example: > The safeguard caused the reduction in misuse. Required evidence: - Causal design or strong causal reasoning - counterfactual - confounder assessment - timing - mechanism - sensitivity analysis - alternative explanations Correlation alone is insufficient. ## 9.12 Predictive Claims Example: > This evaluation predicts future real-world incidents. Required evidence: - Defined prediction - prospective or historical validation - outcome measure - time horizon - calibration - base rates - uncertainty - out-of-sample testing ## 9.13 Forecast Claims Required evidence: - Forecaster identity - information date - target definition - time horizon - probability - rationale or model - calibration history where available - update rule Forecasts should remain distinct from observed facts. ## 9.14 Organizational-Practice Claims Example: > The organization has an effective incident-response program. Required evidence: - Policies - roles - training - exercises - actual incidents - response records - corrective actions - independent assessment Documentation alone is weak evidence of effectiveness. ## 9.15 Independence Claims Required evidence: - Governance - funding - client relationships - selection process - access - method freedom - publication rights - conflicts - actual examples of unfavorable findings ## 9.16 Competence Claims Required evidence: - Scope - qualifications - experience - work samples - proficiency - quality controls - continuing competence ## 9.17 Audit Claims Required evidence: - Criteria - scope - period - auditor - independence - procedures - findings - limitations - status ## 9.18 Certification Claims Required evidence: - Scheme - requirements - certified object - certification body - scope - decision - validity - surveillance - registry status Certification does not establish properties outside scope. ## 9.19 Accreditation Claims Required evidence: - Accreditation body - standard or criteria - accredited body - exact scope - effective date - status - suspension or withdrawal information ## 9.20 Compliance Claims Required evidence: - Applicable law, regulation, contract, or rule - jurisdiction - date - facts - qualified interpretation - scope - unresolved questions Standards Body should not issue legal opinions without authorized legal expertise. ## 9.21 Policy Claims Example: > A requirement should be mandatory. Required evidence: - Technical need - public-interest rationale - alternatives - costs - benefits - distributional effects - capacity - legal authority - implementation - uncertainty - values and tradeoffs Technical evidence informs policy. It does not uniquely determine it. ## 9.22 Consensus Claims Required evidence: - Defined expert or stakeholder population - process - participation - agreement level - substantial objections - dissent Avoid: - Experts agree - global consensus - industry consensus without documentation. ## 9.23 Historical Claims Required evidence: - Contemporaneous primary record where available - date - source context - later correction - distinction between fact and interpretation ## 9.24 Reputation Claims Example: > Organization X is the leading evaluator. Required evidence: - Defined criterion - comparison set - period - source - conflicts Prefer measurable descriptions over vague rankings. ## 9.25 Public-Impact Claims Example: > The standard reduced harm. Required evidence: - Baseline - adoption - implementation - outcome - attribution - displacement - unintended effects - distribution --- # 10. Technical Evaluation Evidence ## 10.1 Minimum Technical Record A technical evaluation record should include: - Evaluation purpose - claim - model and system identity - protocol identifier and version - task source and sampling - held-out status - elicitation conditions - tools and scaffolds - environment - retries - parameters - scoring - invalid runs - uncertainty - baseline - date - evaluator - conflicts - limitations - result status ## 10.2 Raw Evidence Where security and privacy permit, preserve: - Inputs - outputs - trajectories - logs - timestamps - environment state - scoring records - reviewer annotations - code version - configuration ## 10.3 Failed and Excluded Runs Document: - Failure reason - exclusion rule - count - effect on result - whether exclusion was pre-specified Selective removal of failures is prohibited. ## 10.4 Task Sampling Evidence should state: - Target task universe - sampling method - sample size - exclusions - difficulty - representativeness - exposure status ## 10.5 Elicitation Evidence should state: - Prompting - examples - fine-tuning - tools - human assistance - search - retries - compute - time - optimization effort ## 10.6 System Configuration Record all material components. A base-model result should not be presented as a full-system result unless the configuration is equivalent. ## 10.7 Scoring Evidence Scoring should include: - Rule - rubric - judge - judge version - human review - agreement - ambiguity - appeal - score changes ## 10.8 Model-Based Judges Model-based scoring should identify: - Judge model - version - prompt - calibration - human-validation sample - conflict or shared lineage - bias and failure analysis ## 10.9 Human Judges Human scoring should identify: - Qualifications - training - blinding - conflicts - agreement - adjudication - workload - compensation ## 10.10 Statistical Evidence Where applicable, report: - Sample size - estimate - uncertainty interval - distribution - repeated-measures structure - missing data - multiple comparisons - effect size - sensitivity analysis ## 10.11 Qualitative Technical Evidence Qualitative evidence may include: - Failure narratives - trajectory analysis - expert review - red-team observations - emergent strategy - unusual behavior It should be systematically recorded rather than treated as anecdote alone. ## 10.12 Result Expiration Technical evidence should expire or be reviewed after: - Material model update - system change - protocol change - task compromise - new elicitation method - major incident - scheduled period --- # 11. Organizational and Institutional Evidence AI assurance depends on both technical and organizational evidence. ## 11.1 Policy Versus Practice A policy proves that an organization documented an intention or requirement. It does not prove: - Implementation - effectiveness - consistency - enforcement - continuous operation ## 11.2 Organizational Evidence Types Relevant evidence may include: - Policies - governance charters - role descriptions - training records - access logs - incident records - audit trails - budgets - staffing - committee minutes - decision records - complaints - corrective actions - exercises - deployment records - performance metrics ## 11.3 Practice Evidence Stronger practice evidence includes: - Repeated operational records - sampled case files - observed exercises - incident handling - independent interviews - system logs - correction history - evidence of unfavorable decisions ## 11.4 Governance Claims Claims about governance should examine: - Actual decision rights - escalation - conflicts - recusals - dissent - oversight - accountability - emergency use - documented outcomes ## 11.5 Culture Claims Claims such as "the organization has a strong safety culture" require caution. Possible evidence: - Reporting behavior - retaliation controls - near-miss disclosure - staff surveys - decision records - resource allocation - leadership conduct - treatment of dissent - correction history ## 11.6 Resource Claims A policy may fail because: - Staffing is insufficient - expertise is absent - compute is unavailable - security is weak - evaluation time is compressed - budgets are unstable Evidence should examine practical capacity. ## 11.7 Independence Claims Independence should be evidenced through structure and behavior. Strong evidence includes: - Governance separation - protected publication rights - diversified funding - non-result-dependent fees - conflict controls - unfavorable findings - documented recusals - external oversight ## 11.8 Competence Claims Evidence of competence should match scope. A general AI credential is not sufficient evidence of: - Cyber expertise - biological expertise - standards competence - statistical competence - legal competence - secure evaluation competence ## 11.9 Organizational Sampling When evaluating an organization, define: - Units - locations - teams - time period - systems - records - selection method - exclusions ## 11.10 Management-System Evidence Management-system evidence can support claims concerning: - Governance - process - documented controls - continuous improvement - internal review It should not be treated as direct proof of every product or system outcome. --- # 12. Legal, Regulatory, and Standards Evidence ## 12.1 Authoritative Legal Sources Prefer: - Enacted text - official regulation - court decision - agency order - official guidance - legislative or treaty record - official effective-date and status page ## 12.2 Legal Status Record: - Jurisdiction - authority - version - effective date - amendments - transition - current status ## 12.3 Proposed Law Clearly distinguish: - Proposal - introduced bill - adopted text - effective requirement - guidance - enforcement action ## 12.4 Legal Interpretation Legal interpretation should identify: - Qualified author - jurisdiction - assumptions - contested questions - date - whether the statement is advice, analysis, or official decision ## 12.5 Standards Evidence For a standards claim, record: - Issuing body - standard number - title - edition - status - scope - access date - whether the standard is voluntary - whether it has been incorporated into law or contract ## 12.6 Standards Summaries An official summary may support general description. It should not replace the standard text when exact requirements matter. ## 12.7 Copyrighted Standards Where full standards text cannot be reproduced: - Cite the official record - quote only within lawful limits - summarize carefully - identify interpretation - avoid implying access not possessed ## 12.8 Certification and Accreditation Status Use official registries or issuing-body records where available. Check: - Scope - site - certificate number - status - expiration - suspension - withdrawal ## 12.9 Legal Crosswalks A standards crosswalk does not establish legal equivalence unless the competent authority recognizes it. ## 12.10 Temporal Stability Legal and standards claims should be rechecked before publication because: - Text changes - implementation dates change - standards are revised - certifications expire - guidance is replaced - court decisions alter interpretation --- # 13. Policy and Normative Evidence ## 13.1 Fact Versus Value Policy analysis should distinguish: - Descriptive fact - technical inference - forecast - value judgment - institutional preference - legal constraint - policy recommendation ## 13.2 Evidence Does Not Eliminate Values Questions such as acceptable risk, rights, distribution, and public authority contain normative judgments. Standards Body should make those judgments visible. ## 13.3 Policy Option Analysis A policy recommendation should examine: - Problem definition - evidence - alternatives - expected benefits - expected costs - uncertainty - distributional effects - implementation - enforcement - institutional capacity - reversibility - international effects - failure modes ## 13.4 Stakeholder Evidence Stakeholder experience can provide direct evidence concerning: - Deployment impact - access barriers - operational failure - rights - burden - local context It should not be dismissed because it is not quantitative. ## 13.5 Public Opinion Public opinion is evidence of: - Preference - concern - legitimacy - perceived impact It is not direct evidence of technical validity. ## 13.6 Economic Evidence Economic claims should identify: - Model - assumptions - counterfactual - price or cost basis - time horizon - sensitivity - distribution ## 13.7 International Comparison Cross-jurisdiction claims should account for: - Legal differences - institutional capacity - market structure - language - culture - enforcement - data quality --- # 14. Expert Judgment Expert judgment is necessary where: - Evidence is incomplete - systems are novel - risks are rare - direct testing is unsafe - interpretation requires deep domain knowledge - decisions cannot wait for complete data ## 14.1 Expert Qualification Record: - Domain - experience - methods - relevant work - current competence - conflicts - institutional role ## 14.2 Structured Judgment Prefer structured methods with: - Defined question - individual estimates before discussion - assumptions - evidence access - confidence - rationale - aggregation rule - disagreement record - update process ## 14.3 Panel Composition Consider diversity in: - Technical discipline - institutional role - geography - methods - worldview - deployment experience - public-interest expertise ## 14.4 Consensus Consensus should not be manufactured through pressure to agree. Record: - Areas of agreement - substantial objections - minority views - confidence - unresolved questions ## 14.5 Expert Elicitation Where experts provide estimates, record: - Target quantity - units - time horizon - probability scale - calibration support - decomposition - aggregation - update date ## 14.6 Expert Judgment Limits Expert status does not eliminate: - Bias - groupthink - overconfidence - reputation incentives - incomplete access - political conflict ## 14.7 Expert Judgment as Evidence Expert judgment should be labeled as such. It should not be silently converted into an observed fact. --- # 15. Confidential and Restricted Evidence ## 15.1 Legitimate Uses Confidential evidence may be necessary for: - Held-out evaluations - security vulnerabilities - model weights - system prompts - internal incidents - proprietary methods - personal data - national-security information - contractual records - unpublished investigations ## 15.2 Confidentiality Does Not Increase Quality Automatically A claim is not stronger merely because the evidence is secret. ## 15.3 Confidential Evidence Requirements Record: - Evidence class - owner - source - date - authorized reviewers - access method - provenance - limitations - retention - public claim supported - review date ## 15.4 Independent Access For consequential public claims, confidential evidence should normally be reviewed by a qualified independent person or body. ## 15.5 Public Disclosure Statement A public report should state: - That confidential evidence was used - the general evidence type - who reviewed it - the review scope - why it is not public - what conclusion is supported - what cannot be independently verified by the public ## 15.6 Confidentiality and Dissent Reviewers should be able to record dissent without disclosing protected details. ## 15.7 Selective Disclosure Do not provide favorable confidential evidence while withholding materially adverse evidence from reviewers. ## 15.8 Security Classification Use a defined classification and handling system. ## 15.9 Sunset and Release Protected evidence should have: - Review date - declassification or release criteria - destruction or archive plan - successor evidence plan ## 15.10 Evidence Access Log Log: - User - purpose - date - action - export - modification - revocation --- # 16. Model-Generated and Tool-Assisted Evidence Work ## 16.1 Permitted Assistance AI tools may assist with: - Search-query generation - source discovery - document extraction - translation support - citation formatting - comparison - classification - consistency checks - draft generation - data analysis under review - code generation under testing ## 16.2 Not Independent Evidence A model-generated statement is not an independent source. It may reflect: - Training data - tool results - prompt assumptions - hallucination - outdated information - citation fabrication - hidden transformation ## 16.3 Verification Requirement Every material model-generated claim should be checked against: - Primary source - trusted data - reproducible computation - qualified human judgment ## 16.4 Citation Rule Cite the underlying source, not the model output, unless the model interaction itself is the object of study. ## 16.5 Analysis Rule Model-assisted analysis should record: - Tool or model - version - prompt or procedure where material - input data - human review - tests - limitations ## 16.6 Sensitive Information Do not submit protected evidence to an external model or service without authorization and appropriate controls. ## 16.7 Automated Extraction Automated extraction should be sampled for: - Omission - transcription error - table error - citation mismatch - context loss ## 16.8 Model-Based Scoring See Section 10.8. ## 16.9 Human Accountability The named author, reviewer, or institution remains responsible for the final work. ## 16.10 Disclosure Material use of AI should be disclosed when it affects: - Method - reproducibility - evidence interpretation - confidentiality - authorship - public trust --- # 17. Uncertainty ## 17.1 Uncertainty Types Evidence reviews should consider: - Measurement uncertainty - sampling uncertainty - model uncertainty - task uncertainty - system-identity uncertainty - contamination uncertainty - elicitation uncertainty - interpretive uncertainty - causal uncertainty - forecast uncertainty - legal uncertainty - institutional uncertainty - unknown unknowns ## 17.2 Quantitative Uncertainty Where valid, report: - Standard error - confidence or credible interval - distribution - sensitivity - scenario range - calibration - probability ## 17.3 Qualitative Uncertainty Use structured explanation when quantification would create false precision. ## 17.4 Sources of Uncertainty List the material sources separately. Avoid one vague limitation statement. ## 17.5 Uncertainty and Decisions A decision may still be justified under uncertainty. The decision record should explain: - What is uncertain - consequence of delay - consequence of error - reversibility - monitoring - update triggers ## 17.6 Uncertainty Reduction Identify which additional evidence would most improve the decision. ## 17.7 Irreducible Uncertainty Some uncertainty may remain because: - Systems are adaptive - deployment is open-ended - harmful events are rare - evidence is protected - future actors are unknown - models change ## 17.8 False Precision Do not assign a precise probability or score when the evidence cannot support it. ## 17.9 Measurement Guidance Standards Body draws on established measurement practice that treats uncertainty as a required component of credible measurement rather than an afterthought.[^gum] --- # 18. Confidence Ratings Confidence expresses the strength of justified belief in a bounded conclusion. ## 18.1 Very Low Confidence Conditions may include: - E0 or weak E1 evidence - major evidence gaps - indirect or unverifiable sources - unresolved contradictions - unstable system identity - severe conflict - high uncertainty Permitted wording: - Speculative - unverified - insufficient evidence - early signal only ## 18.2 Low Confidence Conditions may include: - Preliminary direct evidence - limited sample - weak replication - important access limitations - substantial alternative explanations ## 18.3 Moderate Confidence Conditions may include: - E2 support - relevant direct evidence - some corroboration - bounded claim - known limitations - no decisive contrary evidence ## 18.4 High Confidence Conditions may include: - E3 evidence - strong method - current system identity - independent challenge - reproducibility or replication - uncertainty analysis - contrary-evidence review ## 18.5 Very High Confidence Conditions should normally include: - Extensive E3 or E4 evidence - repeated independent support - stable construct - strong provenance - low material uncertainty - no credible unresolved contradiction - mature operational experience Very high confidence should be uncommon for frontier AI capability, safety, and forecasting claims. ## 18.6 Confidence Is Claim-Specific A report may support: - High confidence that a test was conducted correctly - moderate confidence that the test measures the intended capability - low confidence that the result predicts real-world harm ## 18.7 Confidence Changes Confidence should decrease when: - System changes - evidence expires - contamination is suspected - a contrary study appears - evaluator status changes - an incident reveals a gap ## 18.8 Confidence Record Record: - Label - rationale - evidence level - key supporting evidence - key contrary evidence - uncertainty - reviewer - date --- # 19. Evidence Synthesis ## 19.1 Synthesis Purpose Evidence synthesis should answer: - What is supported? - What is not supported? - Where does evidence disagree? - Why? - What remains unknown? - What decision follows? ## 19.2 Synthesis Methods Methods may include: - Narrative synthesis - systematic review - meta-analysis - structured expert judgment - evidence matrix - causal analysis - case comparison - triangulation - bridge study ## 19.3 Selection Criteria State: - Search scope - inclusion - exclusion - date range - source types - languages - security limitations ## 19.4 Evidence Table A synthesis should include a table or structured register with: - Source - claim - method - system - date - quality - conflict - result - limitation - weight ## 19.5 Contrary Evidence Search Actively search for: - Failed replications - negative results - alternative explanations - incidents - critiques - withdrawn work - conflicting jurisdictions - evaluator disagreement ## 19.6 Weighting Weight should consider quality dimensions, not source count alone. ## 19.7 Heterogeneity Do not combine evidence when: - Constructs differ - systems differ materially - conditions differ - score meaning differs - legal contexts differ - evidence status differs ## 19.8 No Forced Consensus Preserve unresolved conflict. ## 19.9 Living Synthesis High-change topics should use: - Review dates - update triggers - status records - source monitoring - version history --- # 20. Reproducibility, Replicability, and Independent Challenge ## 20.1 Reproducibility Standards Body uses *reproducibility* for obtaining consistent computational or analytical results using the same data, code, methods, and conditions. A reproducibility package should include, where lawful and safe: - Data or data-access instructions - code - environment - dependencies - configuration - model version - protocol - random seeds - execution instructions - scoring - expected outputs - known nondeterminism The National Academies distinguishes computational reproducibility from replicability using new data or independent methods, a distinction adopted here because it clarifies what kind of independent support actually exists.[^nasem-repro] ## 20.2 Replicability Replicability asks whether materially independent work supports the finding. Replication may use: - New task samples - new environments - another evaluator - another implementation - another system version - another jurisdiction - another language ## 20.3 Reperformance Reperformance is especially relevant to: - Audits - scoring - evidence extraction - control testing - calculations - certification decisions ## 20.4 Conceptual Replication A different method may test the same underlying construct. This may provide stronger generalization evidence than exact repetition. ## 20.5 Protected Reproducibility Held-out or confidential evidence may be reproduced through: - Secure enclaves - independent custodians - controlled-access repositories - authorized reviewer sessions - split custody - signed result artifacts ## 20.6 Reproducibility Failure A failure to reproduce may result from: - Missing dependencies - model version drift - undocumented prompts - stochastic variation - inaccessible data - scoring changes - task leakage - analyst error It should trigger investigation, not immediate accusation. ## 20.7 Replication Failure A failed replication may reflect: - Original error - changed construct - different population - underpowered study - protocol difference - system change - publication bias ## 20.8 Independent Challenge When exact reproduction is impossible, qualified reviewers should still be able to challenge: - Method - assumptions - evidence - scoring - uncertainty - interpretation - conflicts - claim scope ## 20.9 Minimum Reproducibility Statement Every major technical paper should state one of: - Fully reproducible from public artifacts - reproducible under controlled access - partially reproducible - not currently reproducible and explain why. --- # 21. Incident, Near-Miss, and Failure Evidence ## 21.1 Incident Evidence Incident evidence may include: - Logs - user reports - system outputs - access records - screenshots - forensic artifacts - internal communications - control failures - response records - downstream impact ## 21.2 Near-Miss Evidence Near misses can reveal: - Weak controls - latent pathways - detection success - accidental containment - untested capability - governance gaps ## 21.3 Incident Validation Confirm: - Event identity - system identity - time - source - impact - causal role - evidence integrity - duplicate reports - confidence ## 21.4 Causation Do not attribute an incident to AI merely because AI was present. Assess whether the system: - Initiated - enabled - accelerated - amplified - failed to prevent - merely coincided with the event. ## 21.5 Root-Cause Evidence Root-cause analysis should distinguish: - Immediate cause - contributing factors - organizational conditions - safeguard failure - human action - model behavior - environmental condition ## 21.6 Incident Severity Severity should be based on: - Actual harm - potential harm - scale - reversibility - affected parties - strategic consequence - recurrence risk ## 21.7 Incident Confidentiality Protect: - Personal data - exploitable vulnerabilities - national-security information - active investigation - confidential business information while preserving public learning where possible. ## 21.8 Incident Feedback Validated incident evidence should update: - Protocols - task banks - threat models - safeguards - standards - evaluator scopes - recognition - public claims ## 21.9 Failure Database `FAILURE_DATABASE.md` should record: - Event - evidence - confidence - source - system - failure mode - root cause - lessons - affected files - correction --- # 22. Temporal Validity and Freshness ## 22.1 Evidence Age Age alone does not determine quality. A stable legal principle may remain current for decades. A model capability result may become obsolete within weeks. ## 22.2 Freshness Categories ### Stable Expected to remain applicable absent formal change. Examples: - Foundational definitions - established measurement principles ### Slow-Changing Review annually or after institutional change. Examples: - Governance structures - standards status - accreditation arrangements ### Moderate-Change Review quarterly or after material development. Examples: - evaluator landscape - policy implementation - deployment practices ### Fast-Changing Verify immediately before use. Examples: - Model availability - system capabilities - software versions - legal implementation dates - certification status - current officeholders - active incidents ## 22.3 Evidence Date Fields Record separately: - Event date - data-collection date - publication date - access date - verification date - review date ## 22.4 Freshness Trigger Recheck evidence after: - Model release - protocol revision - legal amendment - standard revision - certification suspension - major incident - institutional reorganization - public correction - new replication ## 22.5 Historical Evidence Historical evidence remains relevant for: - Trend - institutional learning - prior commitments - failure analysis - precedent It should not be presented as current status. ## 22.6 Stale Evidence Language Use: - Historical - superseded - current as of - not reverified - may no longer apply ## 22.7 Automated Monitoring Automated monitoring may flag changes. A human or authorized process should verify material status changes. --- # 23. Citation and Attribution Standards ## 23.1 Citation Purpose Citations should allow a reader to: - Locate the source - verify the claim - understand the evidence type - assess recency - distinguish source from interpretation ## 23.2 Citation Coverage Cite: - Noncommon factual claims - statistics - legal requirements - standards claims - technical findings - institutional status - direct quotations - historical events - contested claims - forecasts and expert judgments ## 23.3 Primary Citation Where possible, cite the primary source directly. ## 23.4 Secondary Citation A secondary source may be cited for: - Interpretation - synthesis - criticism - context - historical account ## 23.5 Citation Accuracy The cited source must support the exact claim. Prohibited practices: - Citation dumping - citing a source that only mentions the topic - citing an abstract for a detailed claim not contained there - citing a secondary summary as though it were the original result - citing a withdrawn source without status ## 23.6 Quotation Direct quotation should: - Be exact - preserve context - identify source - comply with copyright - avoid selective distortion ## 23.7 Paraphrase Paraphrase should preserve: - Meaning - uncertainty - qualifications - scope ## 23.8 Page and Section References Use page, section, table, figure, paragraph, or clause references where precision matters. ## 23.9 Web Sources Record: - Title - institution - publication or update date - URL - access date - status where relevant ## 23.10 Dynamic Pages For changing pages, preserve: - Archived copy where lawful - screenshot or record - access date - change note ## 23.11 Data and Code Cite: - Dataset - version - repository - code commit - license - access date - preprocessing ## 23.12 Standards Cite the exact standard identifier, edition, title, and status. ## 23.13 Legal Sources Cite the authoritative legal text and jurisdiction. ## 23.14 Personal Communication Use only with permission and clear labeling. ## 23.15 AI Tool Attribution Do not cite an AI assistant as the source of an underlying factual claim. Disclose material AI assistance separately. --- # 24. Public Claims and Communication ## 24.1 Public-Claim Minimum A consequential public claim should communicate: - Subject - scope - evidence - conditions - uncertainty - limitations - date - status - responsible institution ## 24.2 Headline Discipline Headlines should not exceed the body evidence. ## 24.3 Summary Discipline Executive summaries should preserve: - Qualifications - uncertainty - contrary evidence - claim boundaries ## 24.4 Visualizations Charts should include: - Units - source - date - scale - uncertainty - missing data - comparable categories Avoid: - Truncated axes that distort interpretation - combining incomparable protocols - decorative precision - omitted confidence intervals where material ## 24.5 Rankings Do not publish rankings when: - Construct validity is weak - results are not comparable - uncertainty overlaps materially - tasks are easily gamed - the ranking would create harmful incentives ## 24.6 Safety Language Avoid: - Safe - risk-free - harmless - failsafe - secure as absolute labels. ## 24.7 Legal Language Avoid: - Approved - compliant - certified - accredited - official unless the relevant authority, scope, and status are clear. ## 24.8 Confidential-Evidence Language State the role and limits of nonpublic evidence. ## 24.9 Preliminary Evidence Prominently label preliminary findings. ## 24.10 Corrections Public corrections should reach the same channels as the original claim where feasible. --- # 25. Evidence Contradiction and Dispute ## 25.1 Contradiction Types Evidence may conflict because of: - Different system versions - different constructs - different task samples - different elicitation - different environments - different scoring - different access - different time - error - fraud - genuine heterogeneity ## 25.2 Contradiction Procedure 1. Confirm the claims are actually comparable. 2. Verify source authenticity. 3. inspect system and protocol versions. 4. compare methods. 5. compare samples. 6. compare uncertainty. 7. examine conflicts. 8. seek reperformance or replication. 9. preserve unresolved disagreement. 10. update confidence. ## 25.3 Disputed Status A claim may be labeled: - Under review - disputed - partially substantiated - inconclusive - superseded ## 25.4 No Majority-by-Citation Ten weak derivative sources do not outweigh one strong contradictory primary source automatically. ## 25.5 Institutional Disagreement When institutions disagree, document: - Mandates - evidence access - methods - assumptions - thresholds - legal context - dissent ## 25.6 Fraud or Misconduct Allegation Do not make misconduct claims without strong evidence and due process. Distinguish: - Error - negligence - selective reporting - conflict - fabrication - fraud --- # 26. Corrections, Retractions, and Withdrawals ## 26.1 Correction Trigger Correct when: - A factual statement is wrong - a citation does not support a claim - material evidence was omitted - system identity was incorrect - a calculation is wrong - status changed - wording overstates evidence ## 26.2 Correction Types ### Minor Correction Does not change the central conclusion. ### Material Correction Changes scope, confidence, evidence level, or decision implication. ### Retraction or Withdrawal The work should no longer support the claim. ## 26.3 Correction Record Record: - Original - corrected text - reason - date - responsible owner - affected documents - effect on conclusion ## 26.4 Preservation Do not silently delete the prior version where a material public record exists. ## 26.5 Propagation Review dependent: - Website pages - foundation papers - standards proposals - source registries - public statements - partnership materials ## 26.6 Timeliness Correct promptly after verification. ## 26.7 Incentives Correction should be treated as an integrity function, not automatic institutional failure. Concealment and repeated negligence should receive different treatment. --- # 27. Evidence Security and Data Governance ## 27.1 Evidence Security Objectives Protect: - Confidentiality - integrity - availability - authenticity - provenance - authorized use ## 27.2 Access Use: - Role-based access - least privilege - multifactor authentication - access review - revocation - logging ## 27.3 Storage Define: - Approved environment - encryption - backup - geographic or legal constraints - retention - deletion ## 27.4 Transfer Use: - Secure channels - recipient verification - transfer logs - file integrity - onward-disclosure rules ## 27.5 Sensitive Personal Data Minimize and protect personal data. ## 27.6 Evidence Tampering Controls may include: - Hashes - signatures - immutable logs - witnessed transfer - read-only archives - independent copies ## 27.7 Provenance Interchange Machine-readable provenance can draw on general models such as the W3C PROV data model, which represents entities, activities, agents, and their relationships for provenance interchange.[^w3c-prov] ## 27.8 Incident Response Evidence compromise should trigger: - Containment - impact assessment - notification - result review - correction - re-evaluation - control improvement --- # 28. Evidence Review Lifecycle ## 28.1 Question Define the decision and claim. ## 28.2 Plan Define: - Evidence standard - source scope - methods - reviewers - security - timeline ## 28.3 Collect Gather primary, secondary, supporting, and contrary evidence. ## 28.4 Authenticate Verify source, version, status, and provenance. ## 28.5 Assess Evaluate quality dimensions. ## 28.6 Synthesize Integrate evidence and preserve conflict. ## 28.7 Assign Assign evidence level and confidence. ## 28.8 Review Conduct relevant technical, methodological, legal, security, or independent review. ## 28.9 Decide State conclusion, limitations, and decision implication. ## 28.10 Publish or Restrict Apply disclosure classification. ## 28.11 Monitor Track new evidence, incidents, and status changes. ## 28.12 Correct Revise, suspend, withdraw, or retire as needed. --- # 29. Evidence Governance ## 29.1 Governing Functions Standards Body should maintain governance for: - Evidence definitions - source selection - claim approval - confidence ratings - confidential access - corrections - disputes - model-assisted research - citation practice - evidence retention - periodic review ## 29.2 Roles ### Claim Owner Responsible for wording, evidence selection, limitations, and correction. ### Research Lead Responsible for evidence collection and synthesis. ### Domain Reviewer Reviews subject-matter validity. ### Methodological Reviewer Reviews design, measurement, and inference. ### Source Reviewer Checks source authenticity, citation fit, and status. ### Security Reviewer Reviews protected evidence and handling. ### Independent Reviewer Challenges material claims and contrary-evidence handling. ### Publication Owner Approves public wording and status. ### Records Custodian Maintains evidence packages and version history. ## 29.3 Separation For high-stakes claims, the person making the original analysis should not be the only person approving: - Evidence level - confidence - public wording - correction - withdrawal ## 29.4 Conflict Disclosure Reviewers should disclose material: - Financial - organizational - intellectual - professional - political - personal conflicts ## 29.5 Evidence Committee A future Evidence Standards Committee may: - Maintain this file - resolve disputed evidence classifications - review E4 claims - approve high-stakes public language - supervise corrections - publish guidance - audit evidence practice ## 29.6 Appeals A contributor or affected party may appeal: - Evidence exclusion - confidence rating - public wording - correction decision - source characterization - conflict handling ## 29.7 External Challenge Canonical claims should support external critique through: - Public source lists - methods - claim registers - comment channels - correction processes - controlled access where necessary ## 29.8 Evidence Audit Periodic evidence audits should sample: - Citation accuracy - source quality - freshness - primary-source use - contrary evidence - confidential-evidence governance - AI-tool disclosure - correction propagation --- # 30. Evidence Maturity Model ## Level 0: Assertion-Led Characteristics: - Claims precede evidence - sources are absent or decorative - authority relies on branding or prestige - no correction process ## Level 1: Source-Documented Characteristics: - Material claims have sources - primary and secondary evidence are distinguished - dates and versions are recorded - preliminary status is visible ## Level 2: Quality-Assessed Characteristics: - Evidence dimensions are reviewed - claim types have minimum requirements - contrary evidence is sought - uncertainty and confidence are stated - model-generated work is verified ## Level 3: Independently Reviewed Characteristics: - Consequential claims receive methodological and domain review - confidential evidence is governed - reproducibility status is declared - corrections propagate - evidence packages are maintained ## Level 4: Decision-Grade Characteristics: - Evidence standards are tied to consequence - E4 packages support specified decisions - false-positive and false-negative costs are assessed - security and chain of custody are strong - appeals and expiration exist ## Level 5: Adaptive Evidence Institution Characteristics: - Evidence is monitored continuously - stale claims are automatically flagged - incidents update evidence standards - public claim registers are current - evidence audits occur - standards evolve based on measured outcomes --- # 31. Implementation Pathway ## Phase 1: Claim Inventory Identify all major claims in: - Foundation papers - website copy - standards proposals - public descriptions - institutional documents Assign claim IDs and owners. ## Phase 2: Source Registry Create `SOURCES.md` with: - Source ID - title - author or institution - source tier - date - version - subject - URL or location - access - status - related claims ## Phase 3: Evidence-Level Pilot Select twenty material claims. Assign: - Evidence level - confidence - supporting evidence - contrary evidence - review date Compare reviewer consistency. ## Phase 4: Citation Audit Audit existing canonical documents for: - Unsupported facts - outdated sources - secondary-source substitution - citation mismatch - missing status - unsupported authority ## Phase 5: Evidence Package Pilot Build one complete evidence package for a high-stakes capability claim. ## Phase 6: Confidential-Evidence Procedure Establish: - Classification - access - independent review - public disclosure statement - retention - incident response ## Phase 7: Correction System Create: - Correction request - triage - severity - approval - publication - propagation - archive ## Phase 8: Research Method Integration Apply this standard through `RESEARCH_METHODOLOGY.md`. ## Phase 9: Standards Process Integration Require evidence-readiness review in `STANDARDS_DEVELOPMENT_PROCESS.md`. ## Phase 10: Public Evidence Register Publish bounded information concerning: - Canonical claims - evidence level - confidence - review date - correction status Security-sensitive evidence remains controlled. --- # 32. Evidence Standards Scorecard | Dimension | Core question | |---|---| | Claim definition | Is the claim specific, bounded, and testable? | | Claim owner | Is someone responsible for evidence and correction? | | Decision link | Is the intended use clear? | | Relevance | Does the evidence address the actual claim? | | Directness | Is the inference distance understood? | | Construct validity | Does the evidence measure the intended property? | | Internal validity | Does the design support the conclusion? | | External validity | Is generalization justified? | | Reliability | Is the result consistent under equivalent conditions? | | Provenance | Can origin and history be traced? | | Authenticity | Is the evidence genuine and unaltered? | | Completeness | Are failures, exclusions, and contrary results included? | | Independence | Are producer interests and external challenge clear? | | Recency | Is the evidence current for the object and decision? | | Representativeness | Does the evidence cover the relevant population or task universe? | | Reproducibility | Can the work be recomputed or re-executed? | | Replicability | Does independent evidence support the finding? | | Security | Was the evidence protected from leakage and tampering? | | Uncertainty | Are material uncertainties stated or quantified? | | Contrary evidence | Was disconfirming evidence actively considered? | | Conflict | Are material conflicts disclosed and managed? | | Source quality | Are authoritative primary sources used where available? | | Citation accuracy | Does each citation support the exact claim? | | Confidential evidence | Is nonpublic evidence independently governed? | | Model assistance | Was AI-assisted work verified and disclosed where material? | | Confidence | Is the confidence rating justified? | | Evidence level | Is the evidence level appropriate and documented? | | Public wording | Does the public claim remain within evidence? | | Status | Is the evidence current, corrected, suspended, or withdrawn? | | Expiration | Is re-review or expiration defined? | | Correction | Can error be corrected and propagated? | | Proportionality | Is the burden appropriate to the consequence? | | Decision readiness | Is the package sufficient for the specified use? | ## 32.1 Critical Failures The following normally prevent an E3 or E4 classification: - Unidentified evaluated object - material citation mismatch - unaddressed task compromise - hidden exclusion of adverse results - no uncertainty for a sensitive quantitative claim - decisive conflict without mitigation - unsupported legal or certification claim - material contrary evidence ignored - unverifiable confidential evidence without qualified review - model-generated claim treated as independent evidence - stale evidence applied to a materially changed system ## 32.2 No Universal Evidence Score Do not convert the scorecard into one universal numerical rating. A critical failure should remain visible. --- # 33. Claim and Evidence Register Template **Project:** **Record owner:** **Review date:** | Field | Entry | |---|---| | Claim ID | | | Exact claim | | | Claim type | | | Intended use | | | Consequence if wrong | | | Subject or evaluated object | | | Scope | | | Time period | | | Evidence level | | | Confidence | | | Primary evidence | | | Supporting evidence | | | Contrary evidence | | | Assumptions | | | Uncertainty | | | Conflicts | | | Security status | | | Reviewer | | | Public wording | | | Expiration | | | Status | | | Correction history | | --- # 34. Evidence Source Record Template **Source ID:** **Title:** **Author or institution:** **Source type:** **Source tier:** **Publication or creation date:** **Version:** **Status:** **URL or controlled location:** **Access date:** ## Relevance ## Claims Supported ## Claims Challenged ## Method ## Evaluated Object ## Provenance ## Independence and Conflicts ## Limitations ## Security Classification ## Review Notes ## Superseding Source --- # 35. Evidence Quality Assessment Template **Evidence object:** **Claim:** **Reviewer:** **Date:** Rate each dimension as: - Strong - adequate - limited - weak - not assessed ## Dimensions - Relevance - directness - construct validity - internal validity - external validity - reliability - provenance - authenticity - completeness - independence - recency - representativeness - reproducibility - replicability - security - uncertainty - decision relevance - conflict exposure - gaming susceptibility - legal and ethical fitness ## Critical Limitations ## Contrary Evidence ## Overall Weight ## Evidence Level Recommendation ## Additional Evidence Needed --- # 36. Confidential Evidence Review Template **Evidence package ID:** **Classification:** **Owner:** **Claim supported:** **Review purpose:** ## Authorized Reviewers ## Reviewer Qualifications ## Access Method ## Evidence Types ## Provenance and Custody ## Completeness Statement ## Material Contrary Evidence ## Security Limitations ## Independent Findings ## Publicly Supportable Conclusion ## Publicly Unverifiable Components ## Retention and Review Date ## Release, Destruction, or Archive Plan --- # 37. Confidence Assessment Template **Claim:** **Evidence level:** **Reviewer:** **Date:** ## Supporting Evidence ## Contrary Evidence ## Material Uncertainty ## System or Context Stability ## Reproducibility and Replication ## Conflict Assessment ## Confidence Label - Very low - low - moderate - high - very high ## Rationale ## Conditions That Would Increase Confidence ## Conditions That Would Decrease Confidence ## Review Trigger --- # 38. Correction and Withdrawal Template **Record or publication:** **Version:** **Claim ID:** **Date identified:** **Reporter:** ## Issue ## Evidence ## Severity - Minor - material - invalidating ## Original Wording or Result ## Corrected Wording or Result ## Effect on Evidence Level ## Effect on Confidence ## Effect on Decisions ## Affected Documents ## Public Notice ## Approval ## Completion Date --- # 39. Decision-Grade Evidence Package Template ## A. Decision - Decision owner - authority - question - alternatives - consequence of error - timing ## B. Claim - Exact wording - scope - system - period - evidence standard ## C. Technical Evidence - Protocol - result - raw records - uncertainty - reproducibility - integrity ## D. Organizational Evidence - Governance - controls - competence - operations - incidents ## E. Independent Review - Mandate - reviewers - access - findings - dissent - conflicts ## F. Contrary Evidence - Sources - findings - resolution - remaining disagreement ## G. Legal and Policy Context - Jurisdiction - applicable requirements - authority - normative considerations ## H. Evidence Rating - Evidence level - confidence - rationale - expiration ## I. Decision Implications - Supported decision - conditions - monitoring - appeal - reversal ## J. Public Record - Public claim - confidential-evidence statement - limitations - status - correction channel --- # 40. Canonical Standards Body Positions Standards Body adopts the following working positions. 1. Evidence quality is claim-specific. 2. Evidence sufficiency is decision-specific. 3. A claim should not exceed the scope, certainty, recency, or authority of its evidence. 4. Primary sources should anchor legal, standards, institutional-status, and direct technical claims when available. 5. Secondary sources are valuable for synthesis and criticism but should not replace authoritative primary material without reason. 6. Search snippets, unsourced aggregators, and model-generated summaries are not sufficient evidence for material claims. 7. Peer review increases confidence in process but does not guarantee correctness or replicability. 8. Quantitative evidence is not automatically stronger than qualitative evidence. 9. Qualitative evidence should be collected and analyzed systematically. 10. Developer evidence may be direct and important but requires conflict-aware interpretation and external challenge for consequential claims. 11. External evidence is not automatically independent evidence. 12. Confidential evidence can support a public claim only under stronger governance and review. 13. Confidentiality should not be used to launder unsupported conclusions. 14. Model-generated content may assist analysis but is not an independent factual source. 15. Human or institutional authors remain accountable for model-assisted work. 16. Capability claims should identify the system, protocol, conditions, resources, and limitations. 17. A failed capability evaluation normally supports "not demonstrated," not "absent." 18. Safety claims should be replaced by bounded risk and safeguard claims. 19. Security claims should state the threat model. 20. Comparative claims require comparable objects, protocols, resources, and conditions. 21. Causal claims require causal evidence or explicit causal reasoning. 22. Forecasts should be labeled as forecasts and include time horizon, probability, and information date. 23. Organizational policies are evidence of documented intent, not automatic evidence of effective practice. 24. Certification is evidence only within the certification scheme and scope. 25. Accreditation is evidence of conformity-assessment competence within scope, not universal institutional quality. 26. Legal and regulatory claims should be checked against current authoritative sources. 27. Policy recommendations should distinguish technical evidence from value and authority judgments. 28. Contrary evidence should be actively sought and preserved. 29. Source count should not substitute for source quality. 30. Evidence contradiction should be investigated before results are averaged or one source is dismissed. 31. High-stakes claims should use evidence portfolios rather than one metric. 32. Material failed and excluded evaluation runs should be documented. 33. Elicitation conditions are part of capability evidence. 34. System configuration is part of result identity. 35. Evidence should carry uncertainty. 36. Very high confidence should be rare in frontier AI claims. 37. Evidence should expire or be reviewed after material change. 38. Reproducibility status should be declared for major technical work. 39. Replication and independent challenge should be proportionate to consequence. 40. Provenance should be preserved in human-readable and, where useful, machine-readable form. 41. Incident and near-miss evidence should update protocols, standards, and safeguards. 42. Public summaries should preserve material limitations. 43. Headlines and visualizations should not overstate the underlying evidence. 44. Rankings should not be published when comparability and construct validity are weak. 45. Corrections should be visible, timely, and propagated. 46. Withdrawal is appropriate when evidence no longer supports the claim. 47. Evidence governance should include conflicts, appeals, security, and records. 48. Decision-grade evidence should include the consequence of false positives and false negatives. 49. Evidence burden should remain proportionate and should not unnecessarily exclude smaller actors. 50. Standards Body should evaluate its own evidence practices through recurring audits and public correction. --- # 41. Relationship to the Eight Foundations ## Foundation 1: Dynamic Evaluation Protocols This file defines the evidence required to justify protocol design, change, comparison, and retirement. ## Foundation 2: Held-Out Evaluations This file defines evidentiary treatment of protected tasks, confidential findings, provenance, chain of custody, and compromise. ## Foundation 3: High-Stakes Capability Evaluation This file defines stronger evidence levels, confidence, decision-grade packages, and absence-claim limits. ## Foundation 4: Independent Expert Review This file defines what reviewers should inspect and how independent findings affect evidence weight. ## Foundation 5: Third-Party Auditor Ecosystem This file distinguishes testing, evaluation, audit, certification, accreditation, and competence evidence. ## Foundation 6: Progressive Standards and Requirements This file supplies evidence-readiness requirements for moving from research to guidance, standards, procurement, or binding rules. ## Foundation 7: Incentives and Prestige This file prevents evidence metrics, recognition, and correction systems from rewarding superficial claims. ## Foundation 8: Global Interoperability This file defines the metadata, provenance, confidence, status, and recognition information needed for evidence portability. --- # 42. Final Evidence Position Standards Body is an evidence institution before it is a standards institution. A standards project that cannot distinguish: - Direct evidence from interpretation - evaluation from assurance - preliminary findings from decision-grade findings - absent from not demonstrated - confidential evidence from unverifiable assertion - legal authority from technical recommendation - reproducibility from repetition - confidence from certainty - correction from concealment cannot credibly develop standards for frontier AI. The purpose of evidence standards is not to create a ritual of citations or an appearance of scientific caution. It is to make claims reviewable. Every consequential claim should allow a qualified reader to determine: - What exactly is being claimed? - Which object and version does it concern? - What evidence supports it? - What evidence challenges it? - How was the evidence produced? - Who had access? - Who reviewed it? - What conflicts exist? - What uncertainty remains? - How current is it? - What decision can it support? - When should it be revisited? - Who will correct it if it is wrong? The defining evidence rule of Standards Body is: > **Make the claim bounded, make the evidence traceable, make the uncertainty visible, and make correction possible.** --- # References and Research Basis [^nist-rmf]: National Institute of Standards and Technology, **Artificial Intelligence Risk Management Framework (AI RMF 1.0)**, NIST AI 100-1, 2023. https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf [^nist-genai]: National Institute of Standards and Technology, **Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile**, NIST AI 600-1, 2024. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf [^nist-tev]: National Institute of Standards and Technology, **AI Test, Evaluation, Validation and Verification (TEVV)**. https://www.nist.gov/ai-test-evaluation-validation-and-verification-tevv [^nist-zero-draft]: National Institute of Standards and Technology, **Outline: Proposed Zero Draft for a Standard on AI Testing, Evaluation, Verification and Validation**, 2025. https://www.nist.gov/document/outline-proposed-zero-draft-standard-ai-testing-evaluation-verification-and-validation [^nist-airc]: National Institute of Standards and Technology, **AI RMF Core**, NIST AI Resource Center. https://airc.nist.gov/airmf-resources/airmf/5-sec-core/ [^iso-17025]: International Organization for Standardization, **ISO/IEC 17025:2017, General Requirements for the Competence of Testing and Calibration Laboratories**. https://www.iso.org/ISO-IEC-17025-testing-and-calibration-laboratories.html [^iso-17029]: International Organization for Standardization, **ISO/IEC 17029:2019, General Principles and Requirements for Validation and Verification Bodies**. https://casco.iso.org/bodies.html [^iso-17020]: International Organization for Standardization, **ISO/IEC 17020:2026, Requirements for Bodies Performing Inspection**. https://www.iso.org/standard/17020 [^iso-17065]: International Organization for Standardization, **ISO/IEC 17065:2012, Requirements for Bodies Certifying Products, Processes and Services**. https://www.iso.org/standard/46568.html [^iso-17011]: International Organization for Standardization, **ISO/IEC 17011:2017, Requirements for Accreditation Bodies Accrediting Conformity Assessment Bodies**. https://www.iso.org/standard/67198.html [^gum]: Joint Committee for Guides in Metrology, **JCGM 100:2008, Evaluation of Measurement Data, Guide to the Expression of Uncertainty in Measurement**. https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf [^nasem-repro]: National Academies of Sciences, Engineering, and Medicine, **Reproducibility and Replicability in Science**, 2019. https://nap.nationalacademies.org/catalog/25303/reproducibility-and-replicability-in-science [^w3c-prov]: World Wide Web Consortium, **PROV-DM: The PROV Data Model** and **PROV-O: The PROV Ontology**, 2013. https://www.w3.org/TR/prov-dm/ and https://www.w3.org/TR/prov-o/ [^oecd-haip]: OECD, **Hiroshima AI Process Reporting Framework**. https://oecd.ai/en/hiroshima [^inspect]: UK AI Security Institute, **Inspect AI**. https://inspect.aisi.org.uk/ --- # Revision Record ## Version 1.0 **Date:** July 16, 2026 **Change type:** Complete foundational edition **Summary:** Establishes the canonical Standards Body evidence standard. Defines foundational propositions, evidence objects, claim architecture, evidence levels, quality dimensions, source hierarchy, claim-specific requirements, technical and organizational evidence, legal and policy evidence, expert judgment, confidential evidence, model-assisted work, uncertainty, confidence, synthesis, reproducibility, incident evidence, freshness, citations, public claims, disputes, corrections, security, lifecycle, governance, maturity, implementation, scorecards, operational templates, canonical positions, foundation interfaces, and primary research basis. **Status:** Approved foundational source.