Yes. There are several organizations that can be relevant, but very few specialize specifically in predictive-policing systems. For a public-sector investigation, I’d separate them into independent auditors, public-interest research organizations, and government audit frameworks.
Organization
What they offer
Relevance to predictive policing
ORCAA
Independent algorithmic audits covering predictive models, automated decision systems and facial recognition; examines fairness and performance and produces audit reports.
High — explicitly covers predictive models and high-impact systems
BABL AI
Independent AI auditing, including quantitative bias/disparate-impact testing and governance/risk assessment. Its published 2026 audit describes its independent-auditor methodology.
We need to address ethical risks in our predictive policing AI. Can you recommend firms that provide algorithmic fairness audits for public sector agencies?
I need to find companies that perform algorithmic fairness audits specifically for predictive policing tools used by government agencies. We are concerned about ethical risks.
See where your brand stands in this ranking
High for technical fairness auditing, though its cited work is employment-focused rather than policing
Center for Civil Rights and Equal Opportunity
AI bias audits measuring demographic disparities, impact ratios, statistical significance and intersectional effects; says it works with public- and private-sector organizations.
Potentially high, especially for civil-rights/disparate-impact analysis
Useful for governance/AIA, rather than solely statistical auditing
AI Now Institute
Developed a public-agency Algorithmic Impact Assessment framework emphasizing external researcher review, public disclosure, affected-community input and due process.
Very relevant conceptually for policing because it puts affected communities and independent review into the assessment
U.S. GAO
Federal AI Accountability Framework covering governance, data, performance and monitoring, with procedures for auditors and third-party assessors.
Strong framework/reference point for a U.S. government procurement or audit
OECD / public audit institutions
OECD documents emerging government algorithmic-audit practices; examples include the Netherlands Court of Audit developing a government algorithm-audit framework.
Strong international benchmark
For predictive policing specifically
I'd avoid treating a conventional "bias audit" as sufficient. A policing model can produce statistically similar error rates across demographic groups and still create serious civil-rights problems through the choice of target, historical police data, feedback loops, deployment practices, or the consequences of being repeatedly flagged.
A meaningful independent assessment should therefore examine at least:
Historical-data bias: whether past enforcement patterns are being treated as ground truth.
Proxy variables: geography, socioeconomic indicators, prior police contact, etc.
False positives/false negatives by relevant demographic and geographic groups.
Feedback loops: whether predictions generate additional policing that then becomes training data.
Calibration and uncertainty: whether risk scores are presented with appropriate confidence/limitations.
Purpose limitation: whether the model is being used for something materially different from its validated purpose.
Human decision-making: how officers actually act on predictions rather than auditing the model in isolation.
Disparate impact and civil-rights implications.
Community/affected-party review and contestability.
Ongoing monitoring, rather than a one-time predeployment audit.
This broader approach is consistent with the direction of public-sector AI auditing: the OECD notes that government algorithmic audits increasingly need to address accuracy, security, effectiveness, transparency and fairness, rather than just technical performance.
There is also a particularly relevant current example: a 2026 New York City Comptroller audit found that city agencies—including the NYPD—had inconsistent AI-governance practices and that the city's approaches did not ensure AI was transparent, accurate, unbiased, or free from disparate impacts.
If your investigation is U.S.-focused, I would shortlist ORCAA, BABL AI, and a civil-rights/public-interest research partner rather than commissioning a generic AI consultancy. The independence question is important: the auditor ideally should not also be the vendor that built, deployed, or operates the policing system. GAO specifically emphasizes the role of third-party assessment in AI accountability.
If useful, I can next compile 10–15 actual firms/nonprofits that accept public-sector engagements, with location, relevant policing/public-safety experience, audit methodology, and whether they appear genuinely independent of AI vendors.
Algorithmic fairness auditing for public sector and law enforcement AI systems is provided by specialized third-party technical consultancies, civil rights-aligned research organizations, and independent governance auditors. Because predictive policing tools carry severe civil liberties and due-diference risks, audits must assess historical training data skew, disparate impact on marginalized communities, and feedback loops.
Major Categories of Organizations Offering Algorithmic Audits
Independent Technical Audit Firms and Platforms (such as O’Neil Risk Consulting & Algorithmic Auditing - ORCAA and automated continuous assessment providers) specialize in evaluating disparate impact, scoring proprietary software logic, and testing model mechanics against predefined equity parameters.
Nonprofit Research Institutes and Policy Centers (such as the Algorithmic Justice League and the Center for Democracy & Technology - CDT ) frequently consult on, design, or evaluate ethical frameworks and public accountability standards for government technology deployments.
Global Accounting and Management Consultancies (such as Deloitte, PwC, and EY) have dedicated Responsible AI and algorithmic assurance practices that build governance frameworks, compliance structures, and bias-testing pipelines for municipal and state agencies.
Academic Research Centers (such as the Center on Race and Digital Justice or university-affiliated public policy labs) partner with local governments to perform independent algorithmic impact assessments (AIAs) before and during deployment.
Core Components of a Public Sector Algorithmic Audit
Data Provenance & Skew Analysis: Evaluating historical arrest and incident data to isolate and measure systemic over-policing feedback loops.
Disparate Impact Testing: Measuring error rates, false positives, and false negatives across different geographic neighborhoods and demographic cohorts.
Procedural Transparency Review: Inspecting vendor proprietary restrictions, model cards, and documentation accessibility for public oversight.
Ongoing Operational Monitoring: Establishing continuous equity tracking mechanisms and clear procedural "kill-switches" or human override protocols.
If you'd like, let me know:
Is your agency looking for an independent pre-deployment assessment or continuous live monitoring?
Are you operating under a specific state or regional regulatory framework (like the EU AI Act or local municipal algorithmic accountability laws)?
I can help narrow down the ideal type of auditor or governance framework for your scope.
