AI Bias Audit

Generate your AI Bias Audit Framework, in minutes

Fairness‑testedBias‑mappedRegulation‑aligned
Audit AI BiasSee sample output

  1. Configure your context

  2. AI drafts your output

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  4. Review and refine

AI‑generated · Requires legal review before use

What you get

Two artefacts. Eight deliverables. One generation.

A single generation produces an Executive Summary Word document with embedded charts for sign-off plus a 9-sheet Excel workbook tailored to your jurisdiction, industry, and stated bias-testing posture.

34-Item Bias Audit Checklist

Structured checklist covering data bias, model fairness, deployment risks, and outcome monitoring across all AI lifecycle stages.

Fairness Testing Protocol

Methodology for testing demographic parity, equalised odds, and individual fairness — mapped to your sector's risk profile.

Governance Audit Trail

Named accountability chain from board to model owner, with sign-off checkpoints and escalation paths for bias incidents.

Monitoring & Remediation Plan

Scheduled bias re-evaluation cadence, drift detection triggers, and a structured remediation workflow for identified issues.

Risk Classification by AI Use Case

Each AI system tiered by bias risk — Unacceptable / High / Limited / Minimal — with applicable testing controls per tier.

Regulation Compliance References

EU AI Act Article 10 data requirements, NIST AI RMF bias guidance, and jurisdiction-specific anti-discrimination laws woven throughout.

Roles & Responsibilities Matrix

Clear ownership for bias testing, model validation, and audit sign-off — from data scientists to compliance leads and the board.

Permitted & Prohibited AI Use Cases

Explicit list of approved AI applications and use cases where bias risk is considered unacceptable for your organisation and sector.

Audit AI Bias

Audit your AI for bias

Fill in your organisation details and answer the 25-question self-assessment. The intake answers drive the risk-tier classification of each AI system, the gap rating on every audit item, and the prioritised remediation plan — the more accurately you answer, the more actionable your bias audit will be.

What you'll receive

Executive Summary (.docx) for sign-off
34-item audit checklist (.xlsx) pre-filled from your intake
Fairness Testing Protocol + RACI matrix
Live Dashboard with native radar + doughnut charts

Organisation details

Appears in the document header. Leave blank to use “Your Organisation”.

Risk Appetite

Optional. Up to 6 tools, separated by commas.

Quick self-assessment (25 questions)

For each item below, indicate whether your organisation currently has this practice in place. Answer for your current state — be honest, gaps are the point. The bias-audit output, risk-tier classification, and remediation actions will tailor to your stated answers rather than generic boilerplate.

AI use case characterisation
  1. We use AI to make decisions about individuals (e.g. employment, credit, healthcare, education, eligibility, prioritisation).EU AI Act Art.6 + Annex III; NIST AI RMF MAP-1.1
  2. Some of our AI decisions are fully automated, with no human review before they take effect.GDPR Art.22; EU AI Act Art.14; NIST AI RMF MAP-2.3
  3. Our AI outputs could plausibly disadvantage individuals based on protected characteristics (race, sex, age, disability, religion, sexual orientation, pregnancy, etc.).EU AI Act Recital 27; NIST AI RMF MEASURE-2.11; OECD AI Principle 1.2
  4. Our AI training data is sourced (in whole or part) from third-party datasets, public sources, or scraped web content.EU AI Act Art.10; ISO 42001 Clause 8.4 + Annex A.10
  5. We process special-category personal data (health, biometric, racial/ethnic origin, religion, sexual orientation, etc.) for AI training or inference.GDPR Art.9; EU AI Act Art.10(5); UK GDPR Sched.1 + DPA 2018 s.10
Current bias-testing maturity
  1. Have you measured demographic parity (equal positive-outcome rates) across protected groups for at least one AI system?NIST AI RMF MEASURE-2.11; ISO/IEC 24027:2021
  2. Have you measured equalised odds (equal true-positive AND true-negative rates) across protected groups?NIST AI RMF MEASURE-2.11; Hardt-Price-Srebro 2016
  3. Have you measured calibration (predicted probabilities matching observed outcomes) across protected groups?NIST AI RMF MEASURE-2.5; ISO/IEC 24027:2021 §6
  4. Have you identified and tested for proxy variables (features that correlate with protected characteristics, e.g. ZIP code → race)?NIST AI RMF MAP-2.3; EEOC Guidance 2023
  5. Do you have a documented rationale for which fairness metric(s) you have selected and why those rather than others?NIST AI RMF GOVERN-1.4 + MEASURE-2.11; ISO 42001 Clause 6.1.4
  6. Do you have a pre-deployment bias gate — an explicit checkpoint that blocks release if fairness thresholds are missed?EU AI Act Art.43 (conformity assessment); NIST AI RMF MANAGE-1.3
  7. Do you have post-deployment bias monitoring — regular checks that disparate impact has not emerged after launch?EU AI Act Art.72 (post-market monitoring); NIST AI RMF MEASURE-2.11
  8. Do you have a documented incident-response procedure for detected bias incidents (containment, communication, remediation)?EU AI Act Art.73 (serious-incident reporting); NIST AI RMF MANAGE-4.1
  9. Do you collect and review user complaints / appeals against AI decisions, with explicit disparate-impact analysis on those complaints?GDPR Art.22(3); ISO 42001 Clause 9.1
  10. Have you published an external bias / fairness statement (public transparency about how you handle AI fairness)?EU AI Act Art.13 + Art.50; OECD AI Principle 1.3
Governance posture
  1. Is there a named accountable individual (with documented role) for bias outcomes across your AI systems?NIST AI RMF GOVERN-1.2; ISO 42001 Clause 5.3
  2. Does your board, audit committee, or senior leadership receive regular reporting on AI bias status and incidents?NIST AI RMF GOVERN-1.5; ISO 42001 Clause 9.3
  3. Do your AI vendor / supplier contracts include explicit bias / fairness obligations and audit rights?NIST AI RMF MANAGE-3.2; ISO 42001 Clause 8.4 + Annex A.10
  4. Do you maintain an audit trail of AI model decisions sufficient to investigate a specific bias incident retrospectively?EU AI Act Art.12 + Art.20; ISO 42001 Clause 7.5 + 8.3
  5. Do you have a documented right-to-explanation / contestability procedure for individuals affected by AI decisions?GDPR Art.22(3); EU AI Act Art.86; LGPD Art.20
Sector-specific questions

Pick an industry above to unlock 5 sector-tailored bias-testing questions (HR / Healthcare / Financial Services / Government / Education / Insurance / generic).

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By industry

Tailored guidance for your sector

Each industry page combines the same ai bias audit framework with sector-specific risks, regulator citations, and bias considerations drawn from the canonical overlay data.

By jurisdiction

Tailored to your regulatory context

Each jurisdiction page anchors the ai bias audit framework on the regulations that apply locally — top laws, key articles, and enforcement status — pulled from our maintained jurisdiction overlay.

Often bought together

Part of 3 bundles

Each bundle is anchored on a specific persona’s buyer journey. Standard sum-of-parts pricing — each tool is still purchased individually at its standard price (no subscription, no bundled checkout).

Your data — what we collect, keep, and share

What we collect

Your tool inputs — jurisdiction, industry, staff size, risk appetite, and an optional organisation name — plus a Stripe-processed payment. No account, no email required. Our hosting platform keeps minimal server logs (IP, timestamp) for security.

How long we keep it

Tool inputs are used to generate your document and are not retained after your session. Payment transaction metadata (ID, amount, timestamp) is kept for seven years, the retention period required by financial-records law.

Does it train any model

No. Your inputs are not used to train our AI models. Generation runs through Anthropic's API, which does not use API inputs for model training by default.

Where it is stored

Frontend on Vercel, backend on Railway, AI generation via Anthropic's API, payment via Stripe's PCI-DSS certified infrastructure. All data in transit is TLS-encrypted.

Full detail in the Privacy Policy.

Built to amplify your in-house expertise

These outputs support, rather than replace, your practitioners. Qualified human review is not optional — it is a core part of the process.

  • AI-generated — a first draft, not a finished legal instrument.
  • Qualified review by in-house or external practitioners required before implementation.
  • Regulation references reflect the position at the date of generation.

Full disclaimer

  1. Built to amplify your in-house expertise

    These documents support, and do not replace, qualified legal, clinical, or compliance practitioners. This output does not constitute legal advice. Consult your qualified in-house or external counsel before implementing or relying on any part of this document.

  2. Regulation currency

    Laws, regulations, and guidance cited are subject to change. Content reflects the position as at the date of generation and may not account for subsequent amendments, enforcement decisions, or judicial interpretations.

  3. Proposed legislation

    Where proposed or draft legislation is referenced — including the EU AI Liability Directive — such references describe legislation that has not been enacted. Its final form, scope, timing, and territorial application remain unconfirmed.

  4. AI output limitations

    AI output can contain errors or omissions. Do not rely on this document as a complete statement of your legal obligations or as a substitute for specialist compliance advice.

FAQ

Common questions about the AI Bias Audit Framework

What jurisdictions does this tool cover?

All 14 jurisdictions: European Union, United Kingdom, United States, Canada, Australia, Singapore, China, Japan, India, Brazil, UAE, Switzerland, South Africa, and Global/International. Each output is cited to the jurisdiction's actual laws and regulations.

Which industries are supported?

All 14 industry sectors: Healthcare, Financial Services, HR & Recruitment, Legal & Professional Services, Education & EdTech, Retail & E-commerce, Manufacturing, Marketing & Advertising, Government, Energy, Transport, Insurance, Technology, and a Universal option for cross-sector use.

Is there a subscription or recurring cost?

No. Pay once per generation, view the output online, and download immediately. No monthly fees, no account required, no recurring charges.

Is the output legally binding or a substitute for legal advice?

No. Every output is an AI-generated starting-point document that amplifies your in-house expertise — it is not a substitute for qualified legal review. We include explicit regulatory citations and review notes; you should have a qualified lawyer or compliance professional sign off before implementation.

What is the refund policy?

We offer a 7-day money-back guarantee if the generated document fails to meet reasonable expectations for your stated jurisdiction and industry. See our refund policy for full details.

What does the Bias Audit framework include?

A risk-tier classification (low / moderate / high-risk AI systems), a fairness testing protocol with statistical thresholds, a RACI matrix for audit responsibility allocation, and a live dashboard tracking audit cycles. Output is Word + Excel for sign-off and operations.

Does it cover NYC AEDT, EU AI Act high-risk, or other specific bias regulations?

Yes — the framework references NYC AEDT (820 ILCS 42), EU AI Act Annex III high-risk obligations, EEOC Technical Assistance Document on AI in employment, NIST AI RMF fairness controls, and US state-specific AI assessment laws (Illinois, Colorado, Texas). Specific citations depend on the jurisdiction + industry combination you select.

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