Free AI Bias Audit Checklist
Audit one AI system for bias before it reaches the people it affects, worded to align with the MEASURE function of the NIST AI Risk Management Framework 1.0 and the management-system requirements of ISO/IEC 42001. Answer a short guided form, then run the audit and download it as Word or PDF. It is free and needs no sign-up. Your details are processed securely to write the audit and are never stored, sold, or used for training.
An AI bias audit is a structured review of one AI system for the ways it could treat groups of people differently, across its training data, its features, its outputs, and the way its results are used. This free tool runs that review from a guided form, worded to align with the NIST AI RMF 1.0 MEASURE function and ISO/IEC 42001, returns an overall concern level of low, medium, or high, and lets you download the checklist as Word or PDF, with no paywall and no sign-up.
Audit details
Describe the AI system and the data behind it. You get a complete bias audit checklist with an overall concern level, ready to download as Word or PDF.
System under audit
Your audit will appear here. Fill in the details on the left and select Generate audit.
How the AI bias audit checklist works
Describe the AI system
Enter your organization and the AI system you want audited, then add what it does, who it affects, and the data behind it.
Run the audit
The tool works through the checklist from your details, worded to align with the NIST AI RMF 1.0 MEASURE function and ISO/IEC 42001, and returns an overall concern level. Results come back in seconds.
Download and validate
Export the audit as a PDF or an editable Word file, then validate each finding against your system and data with real bias testing before you rely on it.
Free, private, and built on NIST AI RMF 1.0 and ISO/IEC 42001
Most audit tools sit behind a lead form or keep a copy of what you enter. This one asks for no sign-up and keeps nothing. Running an audit needs a language model, so your details are sent to our server and processed there, then discarded. The findings are worded to line up with the MEASURE function of the NIST AI Risk Management Framework 1.0 and the management-system requirements of ISO/IEC 42001.
We built this from our own AI governance programme, not from a summary of the standard: see BlockSurvey's AI Policy for how we hold our own systems to the same bar.
Processed, never kept
Your details are processed securely to write the audit and are never stored, sold, or used for training. Keep the form to system-level descriptions and leave secrets, personal data, and PHI out.
No account required
No sign-up and no email wall. A fair-use rate limit is the only thing standing between you and the tool.
Free, real download
The complete audit as an editable Word file or PDF at no cost, not a watermarked sample.
What the audit covers
A structured checklist generated from your details, worded to align with the NIST AI RMF 1.0 MEASURE function and ISO/IEC 42001:
- An overall concern level of low, medium, or high, with the reasoning behind it.
- Training-data bias, including who is over- or under-represented in the data.
- Historical bias baked into labels that reflect past human decisions.
- Proxy features that stand in for a protected attribute even when it is not used.
- Disparate impact across the groups the system could affect differently.
- Evaluation gaps, such as metrics reported in aggregate but never by subgroup.
- Feedback loops where the model's own outputs shape the data it later learns from.
- Human oversight and the point at which a person can review or override a result.
- Transparency to the people affected by the system's decisions.
- Recommended tests and next steps to confirm each finding.
- An advisory legal notice added to the end of the document.
NIST AI RMF 1.0 vs ISO/IEC 42001
A credible bias audit draws on both: the NIST AI RMF 1.0 MEASURE function tells you how to analyse and track a risk like bias, while ISO/IEC 42001 tells you how to run a management system that proves you did. The findings in this audit are worded to feed either one.
| NIST AI RMF 1.0 | ISO/IEC 42001 | |
|---|---|---|
| What it is | A voluntary risk management framework published by NIST. | A certifiable management system standard for AI, published by ISO and IEC. |
| Structure | Four core functions: govern, map, measure, manage. | Plan-do-check-act clauses plus Annex A controls. |
| Role in an audit | Supplies the MEASURE controls this audit is worded to align with. | Supplies the management-system discipline: ownership, review, and evidence. |
| Can you certify? | No. You self-adopt and document your reasoning. | Yes. An accredited body audits and certifies your management system. |
| Relation to law | Referenced widely in US policy; not itself binding. | Maps to EU AI Act obligations and supports GDPR Article 35 impact assessments. |
Built for every team working with AI
Whether you train models or just deployed one you bought, a bias audit is what lets you show a customer, an auditor, or a regulator that you checked the system for fairness. This tool gives you a structured starting point, and it pairs well with an AI survey platform when the data you feed those systems comes from people.
Compliance & privacy teams
Screen an AI system for fairness risks without starting from a blank page.
Legal & risk
Get a framework-aligned checklist to review and validate, not a generic template.
Data science & ML
See where bias tends to hide before you ship the next version of a model.
Product & engineering
Give teams shipping AI a clear set of fairness questions to answer.
Startups adopting AI
Answer enterprise AI questionnaires with an audit you can actually point to.
Vendors selling into enterprise
Show buyers you checked the AI in your product for bias before they did.
A bias audit is only as fair as the data feeding it.
The disparate-impact findings in this audit depend on data that actually represents the people it affects. BlockSurvey is an AI survey platform, encrypted end to end, with a published AI policy you can cite in your own vendor assessment, so the responses you collect are never sold, mined, or used to train models.
