Public Evidence Packet
Anthropic - Acceptable Use Policy
Acceptable Use Policy
f79b51da-5300-447d-adfc-4a7e0a2ee2f9- Screening date
- 09 Nov 2026, 20:40 UTC
- Jurisdiction
- Global
- Risk screening
- Medium · 4/10
- Score trace
- 0 points; unchanged
This packet records PolicyWatcher evidence and AI-assisted screening for one public change. It is not legal advice, a compliance verdict, a certification, or proof that the external source remains unchanged.
- 01Source evidence
- 02Explainability
- 03Governance Relevance
- 04Report Output
Dataset QA · sanitized public view
Source evidence
- Publication gate
- Published
- Retrieval state
- Recorded retrieval available
- Latest retrieval
- 09 Nov 2026, 20:40 UTC
- Sanitized channel
direct- Data status
- Available
- Public snapshot evidence
- Available
Public snapshot fingerprints
38321f645518cb86a6d62debf46ff043dbcbc71eb37298d7619307e70145d90cCaptured 23 Oct 2026, 11:40 UTCc2948cc1bc558ada3ca5192b7e3dd5e2ed318214ecfe7b50614197e3556d5a8eCaptured 09 Nov 2026, 20:40 UTCSource confidence describes recorded retrieval and publication state. It does not rate the provider policy or certify source authenticity.
Source-anchored screening trace
Explainability
Anthropic's Acceptable Use Policy has no significant changes, maintaining its strong stance against harmful AI uses and requiring human oversight for high-risk applications.
- Reason 1+2 score contribution
Extensive prohibitions on harmful AI use reduce overall risk.
- Related KPI
- Regulatory compliance
- Source side
- old snapshot
Verified exact source passage
“Our Universal Usage Standards are prohibitions that apply to all users and use cases.”
- Reason 2+2 score contribution
Mandatory human-in-the-loop for high-risk AI recommendations enhances safety.
- Related KPI
- Automated decisions
- Source side
- old snapshot
Verified exact source passage
“A qualified person must meaningfully review a High-risk AI Recommendation, with the authority to change or modify it, before it is delivered as advice to an individual who might rely on it or before it is used to implement a decision.”
- Reason 3-1 score contribution
Lack of explicit user opt-out for input data training raises privacy concerns.
- Related KPI
- AI training opt-out
- Source side
- Not recorded
Source passage not recordedThis historical reason has no recorded source passage.
Score reasons and deltaScore values are stored AI-assisted screening outputs. Verified anchors confirm only that the quoted passage occurs in the named snapshot; they do not prove the interpretation.
Advisory review map
Governance Relevance
EU AI Act
Official Journal text, 2024
Which recorded policy statements may be relevant to transparency, automated decisions, data use and human oversight review?
- AI training opt-out
- Not Available
- Algorithmic transparency
- Opaque
- Automated decisions
- Partial
- AI bias and fairness
- Mentioned
ISO/IEC 42001
ISO/IEC 42001:2023 overview
Which recorded policy statements may inform an AI management-system review of transparency, risk oversight and independent assurance?
- Algorithmic transparency
- Opaque
- AI bias and fairness
- Mentioned
- Independent audit
- Absent
- Regulatory compliance
- Comprehensive
NIST AI RMF
AI RMF 1.0; NIST revision in progress, checked 2026-07-29
Which recorded policy statements may support Govern, Map, Measure or Manage review questions?
- Algorithmic transparency
- Opaque
- Automated decisions
- Partial
- AI bias and fairness
- Mentioned
- Content moderation
- Opaque
OECD AI Principles
OECD AI Principles, updated 2024
Which recorded policy statements may be relevant to transparency, fairness, accountability and user agency review?
- Consent mechanism
- Implicit
- Algorithmic transparency
- Opaque
- AI bias and fairness
- Mentioned
- Independent audit
- Absent
Mappings identify review relevance between recorded PolicyWatcher KPI fields and framework topics. They are not legal interpretations, conformity assessments, certifications or compliance verdicts.
Exact change-bound files
Report Output
Both files are generated from this exact change ID.
Download exact-change PDF Download exact-change JSON02bb519abecca9ce8b532615ce08b33e248fad127f7d692ea68399e51b623bbcQuestions before reuse
- Does the original Acceptable Use Policy source still match the recorded public snapshot version 4?
- Do the cited source passages support each displayed reason, KPI value and regional note?
- Which advisory framework topics require specialist legal, risk or governance review for this use case?
- Has a later public change superseded this packet before it is reused in a decision or publication?