AI Bias Audit Framework for CN

CN-specific obligations covered

The output is anchored on the regulations that apply to AI deployments in CN. The top frameworks cited:

• Personal Information Protection Law (PIPL 2021)national_law · In force

  Personal information processors must establish a lawful basis for processing, obtain separate consent for sensitive personal information, conduct personal information protection impact assessments for high-risk processing, and provide opt-out mechanisms for automated decision-making affecting individual rights.

• Interim Measures for the Management of Generative AI Services (CAC, 2023)administrative_regulation · In force

  Generative AI service providers must ensure training data is lawfully sourced, implement content filtering to prevent prohibited outputs, label AI-generated content, and obtain a CAC security assessment before launching public services.

• Provisions on the Management of Algorithmic Recommendations (CAC, 2022)administrative_regulation · In force

  Algorithm recommendation service providers must disclose their use of algorithms to users, provide opt-out options for personalised recommendations, refrain from dynamic pricing that discriminates against existing customers, and prominently label algorithmically distributed content.

• Provisions on the Administration of Deep Synthesis Internet Information Services (CAC, 2022)administrative_regulation · In force

  Deep synthesis service providers must implement real-name registration for users, label all synthetic content with a visible AI-generated marker, prohibit creation of content that impersonates real persons without consent, and retain synthetic content logs for sixty days.

How the AI Bias Audit Framework approaches this

You describe your organisation and AI estate, then answer 25 self-assessment questions across four phases (use-case characterisation, current bias-testing maturity, governance posture, and a 5-question sector-specific block tailored to HR / Healthcare / Financial Services / Government / Education / Insurance / Universal). The tool maps your stated posture into a structured, evidence-based bias audit framework ready for your compliance, legal, and AI-governance practitioners.

The Executive Summary Word document is a one-page sign-off artifact — Risk Classification Scorecard, top 5 bias risks tied to specific AI systems, 30/90/365-day path forward, sign-off block, embedded heatmap + doughnut + gauge charts. The detailed Excel workbook is the working remediation instrument: tier each AI tool, pre-filled audit checklist, fairness testing protocol with explicit thresholds, RACI ownership, permitted/prohibited lists, monitoring cadence, action plan, and a live Dashboard. Both are AI-assisted drafting aids intended to accelerate review by qualified practitioners.

What you get

• ✓ Two artefacts, two jobs: Executive Summary (.docx) with embedded charts for board sign-off, Detailed Workbook (.xlsx) for the working remediation tracking — no overlap, no confusion.
• ✓ Intake-driven: every gap rating, risk-tier classification, and remediation action ties back to your stated YES/NO/PARTIAL/UNSURE answer — no generic checklist boilerplate.
• ✓ Sector-specific: HR customers get NYC LL 144 + EEOC 4/5ths-rule probes; healthcare gets clinical-algorithm fairness + FDA SaMD; financial services gets ECOA / Veritas FEAT — automatic per industry.
• ✓ Live dashboard with formulas + native radar + doughnut + per-section completion that auto-refresh as you mark items Done — no regeneration needed to see remediation progress. Tailored to your jurisdiction, industry, organisation size, and risk appetite.

Ready to generate?

$39 · one-time — answer a 6-question intake (including jurisdiction = CN), and download your tailored document immediately.

Also available framed for your sector → see industry-specific pages