Editorial collection

AI Bias Audit Firms 2026: Independent Directory

For HR leaders, employment counsel, insurance compliance officers, and procurement teams that specifically need a bias or fairness audit — not a broader AI risk assessment. Scope includes NYC Local Law 144 annual bias audits for automated employment decision tools, EU AI Act fairness obligations for high-risk systems, Colorado AI Act algorithmic discrimination duties, and voluntary fairness assessments for hiring, lending, insurance, and healthcare AI.

Last verified April 30, 2026

Editorial independence: aicompliancevendors.com does not accept vendor payment for inclusion or ranking. Every pick below is editor-selected against the criteria stated on this page, and every factual claim is traceable to a cited public source.

At a glance

#	Vendor	Best for	HQ	Pricing
1	ORCAA	Bias audits where protected-class data is unavailable and inference is required	New York City, US	contact only	Profile
2	BABL AI	NYC Local Law 144 bias audits and bias work tied to ISO 42001 certification	Iowa City, US	contact only	Profile
3	Holistic AI	Buyers wanting bias audit consulting and ongoing fairness monitoring software from one vendor	London, UK	contact only	Profile
4	Luminos.Law (ZwillGen AI Division)	Bias audits run under attorney-client privilege alongside legal counsel	Washington, DC, US	contact only	Profile

Selection criteria

How we decided which vendors qualify for inclusion.

Firm publishes a bias or fairness audit service on its own website (not only generic AI advisory).
Documented methodology for measuring disparate impact, demographic parity, or comparable fairness metrics.
At least one engagement type addresses a regulatory bias-audit mandate (NYC LL 144, Colorado AI Act, or EU AI Act fairness clauses).
Active operations as of April 2026.

Each firm's public service pages were reviewed for bias-audit methodology, statistical approach, regulatory framing, and industry coverage. Ranking weighs depth of bias-specific methodology (not breadth of unrelated AI services). Firms were excluded if their public materials describe only general AI risk assessment without a documented bias measurement approach.

The ranking

ORCAA

Best for: Bias audits where protected-class data is unavailable and inference is required

Full profile

ORCAA (NYC, founded 2016) is the most bias-specialized firm in this directory. Its Ethical Matrix framework and Pilot platform produce quantitative disparate-impact measurements, including patent-pending inference for race, ethnicity, and gender where the deployer does not collect that data — a recurring obstacle in NYC LL 144 audits. Double-firewall architecture means ORCAA never sees underlying PII. INFER specialization covers insurance fairness; HTI-1 covers health-IT bias. Founded by Cathy O'Neil, author of Weapons of Math Destruction.

Strengths

Patent-pending protected-class inference methodology — practical for NYC LL 144 audits where employers lack demographic data.
Double-firewall privacy architecture: ORCAA processes audits without seeing PII.
Specialized fairness methods for insurance (INFER) and health IT (HTI-1).

Limitations

No self-service monitoring tools; engagements are point-in-time consulting.
No published pricing.

Framework coverage

NYC Local Law 144 (Automated Employment Decision Tools)Colorado Artificial Intelligence Act (SB 24-205)

BABL AI

Best for: NYC Local Law 144 bias audits and bias work tied to ISO 42001 certification

Full profile

BABL AI (Iowa City, founded 2018) publishes a dedicated NYC Local Law 144 service page documenting its three-stage audit process: scoping, fieldwork, final report. Its independent inference methodology mirrors what NYC employers most often need when they cannot collect demographic data directly. BABL AI is also one of the few firms that performs ISO 42001 certification audits, where bias controls are explicit Annex A requirements. Average documented timeline: 2–3 weeks after documentation submission.

Strengths

Dedicated NYC LL 144 service page with documented methodology.
Bias work integrated with ISO 42001 certification audits (Clause 6.1.4 risk treatment).
Three-stage audit process publicly documented.

Limitations

Enterprise-only pricing; no public rates.
US-based; European on-site engagements may add coordination.

Framework coverage

NYC Local Law 144 (Automated Employment Decision Tools)EU Artificial Intelligence Act ISO/IEC 42001:2023 AI Management System GDPR Article 22 — Automated Individual Decision-Making

Holistic AI

Best for: Buyers wanting bias audit consulting and ongoing fairness monitoring software from one vendor

Full profile

Holistic AI (London, founded 2020) is unusual in this directory because it sells both an end-to-end AI governance platform and a bias-audit consulting service. Public materials describe disparate-impact testing, intersectional bias analysis, and fairness monitoring tied to its software. The combined offering can shorten time-to-evidence for organizations that want continuous bias monitoring after the audit, but buyers should evaluate whether independence is preserved when the same vendor sells the audit and the monitoring tool.

Strengths

Combines bias audit services with continuous fairness monitoring software.
Public methodology covers EU AI Act, NIST AI RMF, ISO/IEC 42001, and NYC LL 144.
European HQ aligns with EU AI Act high-risk system obligations.

Limitations

Selling both audit and monitoring software can complicate independence — verify scope at engagement time.
Pricing not publicly disclosed.

Framework coverage

EU Artificial Intelligence Act NIST AI Risk Management Framework ISO/IEC 42001:2023 AI Management System NYC Local Law 144 (Automated Employment Decision Tools)

Luminos.Law (ZwillGen AI Division)

Best for: Bias audits run under attorney-client privilege alongside legal counsel

Full profile

Luminos.Law became the AI Division of ZwillGen in January 2025. The combined firm is the only one in this directory that pairs technical fairness audits with legal counsel — relevant when bias audits are scoped under attorney-client privilege ahead of expected litigation or regulatory inquiry. Documented work covers EU AI Act, NIST AI RMF, Colorado AI Act, and NYC LL 144. Methodology pages are less detailed than ORCAA or BABL AI; verify current scope post-merger.

Strengths

Attorney-client privilege option for sensitive bias audits.
Combined legal-and-technical practice unique among bias auditors.
Washington DC location for proximity to US regulators.

Limitations

Less publicly documented bias methodology than ORCAA or BABL AI.
Service scope evolving after the ZwillGen merger.

Framework coverage

EU Artificial Intelligence Act NIST AI Risk Management Framework Colorado Artificial Intelligence Act (SB 24-205)NYC Local Law 144 (Automated Employment Decision Tools)GDPR Article 22 — Automated Individual Decision-Making

Buyer guidance

Criteria-based recommendations for the most common shortlist scenarios.

For NYC Local Law 144 audits where the deployer cannot collect demographic data, ORCAA's inference methodology is the most direct fit; BABL AI offers a similar inference approach. When the bias audit must support ISO 42001 certification, BABL AI is the most efficient because it performs both. When ongoing fairness monitoring matters as much as the point-in-time audit, Holistic AI's combined offering is worth weighing — with attention to independence. When privilege is required, ZwillGen's AI Division (formerly Luminos.Law) is the only option here.

What we did not include

Transparency about exclusions.

This directory is bias- and fairness-focused. Broad AI audit firms whose public materials do not document a bias-audit methodology are listed separately at /best/ai-audit-firms. Software vendors whose primary product is bias-detection tooling — not third-party audit services — are listed at /best/ai-bias-detection-tools.

Frequently asked

What is an AI bias audit?+

An AI bias audit is an independent assessment that measures whether an AI system produces disparate outcomes across protected groups. NYC Local Law 144 requires an annual independent bias audit for automated employment decision tools and publication of summary results. EU AI Act high-risk systems must demonstrate fairness across the AI lifecycle. Colorado AI Act requires deployers to manage algorithmic-discrimination risk and supply documentation on request.

Do I need a bias audit firm or a bias-detection software tool?+

They serve different purposes. NYC Local Law 144, EU AI Act conformity assessment, and ISO 42001 Stage 2 require independent third-party assessment — software alone does not satisfy these. Bias-detection tools generate evidence and ongoing monitoring between audits. Most regulated buyers use both: a firm for the audit-of-record and a tool for continuous monitoring.

How are bias audits different from general AI audits?+

A general AI audit reviews multiple risk dimensions including security, robustness, governance, and bias. A bias audit is narrower: it measures disparate impact and fairness metrics, often with a specific regulatory framing (NYC LL 144, Colorado AI Act). Some firms — like BABL AI and ORCAA — offer both; their bias audit methodology is documented separately from broader AI audit services.

How do firms measure bias when the deployer does not collect race or gender data?+

Both ORCAA and BABL AI document inference methodologies — typically Bayesian Improved Surname Geocoding (BISG) for race, name-based inference for gender, and similar techniques — to estimate protected-class composition where the deployer does not collect it. NYC LL 144 explicitly permits inference when direct demographic data is unavailable.

Sources

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Last verified April 30, 2026

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