AI transparency — fairness, explainability, deep fake disclosure
Primary statement
AI transparency per ISO 42001 A.6.1.2 (responsible-development objectives — fairness, transparency, robustness, privacy, safety) + A.5.4 (impact on individuals/groups) + EU AI Act Article 8 + Article 50.4 (deep fake disclosure from Aug 2026) + Article 56 GPAI codes of practice + NIST AI RMF + MeitY transparency.
Audit-fatigue payoff
A unified AI transparency programme — responsible-development objectives + impact analysis + deep fake disclosure + code of practice alignment — satisfies AI transparency requirements across all 4 contributing frameworks.
Strictness matrix
Scope
Scope: responsible-development objectives covering fairness + transparency + robustness + privacy + safety. Five dimensions baked into AI design.
Ceiling source: iso42001:A.6.1.2
Rationale: ISO 42001 A.6.1.2 five-dimension scope is comprehensive.
Threshold
Threshold: deployers of deep fakes shall DISCLOSE the content is artificially generated. Binary disclosure obligation from Aug 2026.
Ceiling source: eu_ai_act:Art.50.4
Rationale: EU AI Act Art 50.4 disclosure threshold is uniquely strict.
Method
Method: responsible-development objectives baked into design + impact assessment (A.5.4) + Article 8 high-risk AI compliance + Article 50.4 deep fake disclosure + Article 56 GPAI code of practice alignment + transparency disclosures to affected individuals.
Ceiling source: iso42001:A.6.1.2
Rationale: ISO 42001 A.6.1.2 + EU AI Act Art 8/50.4 combined are the most prescriptive.
Frequency
Responsible-development review: per AI system + on material change. Deep fake disclosure: per output. GPAI code of practice alignment: annual.
Ceiling source: iso42001:A.6.1.2
Rationale: Per-output disclosure with annual alignment is the cadence.
Evidence
Evidence: responsible-development objectives per system + impact assessment + deep fake disclosure implementation + GPAI code of practice alignment evidence.
Ceiling source: iso42001:A.6.1.2
Rationale: ISO 42001 A.6.1.2 evidence is comprehensive.
Auditor test pattern
Step 1: Inspect responsible-development objectives per AI system. Step 2: Verify impact assessment. Step 3: For deep fake / generative AI, verify Art 50.4 disclosure. Step 4: For GPAI providers, verify code of practice alignment.
Common findings
Common findings: (1) Responsible-development aspirational, not baked in; (2) Impact assessment generic; (3) Deep fake disclosure prep not started; (4) GPAI code of practice alignment absent.