Auditing Artificial Intelligence Systems for Bias in Employment Decision-Making

Quick Hits

• Jurisdictions such as California, New York City, Colorado, Illinois, and the European Union (EU) variously require (or plan to require) and encourage bias testing, notices, transparency, and, in some cases, public summaries. AI involvement can create substantial legal risk, even when humans make the final decisions; AI-influenced scores, rankings, or screens can still be treated by regulatory authorities as decision-making, triggering validation, bias-testing, notice, and transparency duties—with “cutoff” uses increasing regulatory scrutiny.
• Legally privileged bias audits can anchor AI governance efforts by channeling audits through legal counsel, maintaining an inventory and classification of tools, setting clear policies and vendor obligations, conducting ongoing monitoring and remediation, and preserving records supporting job-relatedness, business necessity, and “less-discriminatory alternatives” analyses.

Background

AI and algorithmic tools now permeate modern workforce management, touching everything from applicant recruitment and resume screening to onboarding, performance reviews, development, promotions, scheduling, and retention. The legal environment surrounding these systems is expanding rapidly and unevenly, with places such as California, New York City, Colorado, Illinois, and the EU adopting differing approaches. Approaches vary in each of the current or pending laws, ranging from binding requirements to soft‑law guidance. Importantly, liability can arise even when a person signs off on the outcome: regulators and courts may view AI-generated rankings, scores, or screens as part of the employment decision itself, while the use of rigid thresholds or “cutoffs” can invite heightened scrutiny.

Against this backdrop, the regulatory picture is still taking shape: a patchwork of municipal, state, federal, and global measures that differ in scope, definitions, and timing. Emerging frameworks impose varied governance, transparency, and recordkeeping obligations, while existing antidiscrimination, privacy, and consumer reporting laws continue to supply enforcement hooks. Agencies are issuing guidance and bringing early cases, while private plaintiffs are testing theories that treat algorithmic inputs as part of employment decisions, even when human review is involved. Penalties and remedies range from administrative fines to mandated disclosures and restrictions on use, with some regimes claiming extraterritorial reach and short transition periods, creating real uncertainty for multistate and global employers.

Scope and Focus of Auditing

Anti-bias auditing begins by examining whether the tool’s results differ for protected groups at each stage of the process—for example, with regard to resume scores, rankings, who receives interviews, who passes assessments, and who ultimately gets hired. Statistical findings that suggest adverse impact are a warning light, not the finish line. From there, examining the training and reference data, features that might act as stand-ins or “proxies” for protected traits, how features are built, the score cutoffs applied, any settings by location or role, and how recruiters and managers actually use the output or tool are important steps. If impact is found to be present, the next step involves assessing business necessity and whether less discriminatory ways exist to achieve the same goal, considering specific fixes, such as adjusting thresholds, swapping or removing features, training to improve use or consistency, or changing when or how the tool is used.

Effectiveness auditing assesses whether an AI tool actually enhances decision-making in your specific context. It tests if the system performs as advertised, outperforms your current process, and behaves consistently across roles, teams, sites, and time. Practically speaking, that means benchmarking model outputs against structured human evaluations, checking post-decision outcomes (such as performance, retention, quality and error rates, and downstream corrective actions), and validating that the factors driving recommendations are job‑related and predictive of success.

Effectiveness is inseparable from fairness. A tool that is fast or efficient but unevenly accurate across protected groups—or that relies on features correlated with protected traits—can create legal and operational risks. Evaluating accuracy, stability, and business impact, together with adverse-impact metrics, ensures that “better” does not simply mean “cheaper or quicker” and helps surface situations where apparent gains are driven by biased error patterns. In short, effectiveness auditing assesses whether a tool works, for whom it works, and whether it works for the right job‑related reasons.

Best Practice Considerations

An effective AI governance program brings together the applicable stakeholders for each deployment, with legal, HR, and IT at the core. Legal ensures regulatory alignment, privilege where appropriate, defensible documentation, and coordination across antidiscrimination, privacy, and consumer-reporting regimes. HR anchors job-relatedness, consistent application across roles and locations, and integration with established hiring and performance practices. IT is responsible for system architecture, security, access controls, data quality, monitoring, and change management. Around this core, additional contributors can be included as the use case demands.

Leading With Privilege

A foundational best practice involves starting every significant evaluation, audit, and testing effort with counsel. That means legal scopes the questions, engages the right experts, and directs the work so the analysis is covered by attorney-client privilege and/or work product protections. Following completion of the privileged assessment and agreement on corrective actions, nonprivileged regulatory summaries or disclosures can be prepared as a separate project. This preserves privilege over the analysis while ensuring timely compliance with applicable notice and transparency obligations.

Knowing Your Tools

Most organizations rely on more AI and algorithmic tools than they recognize, so it is sound practice to maintain a living inventory across the talent life cycle—including sourcing databases, resume screens, rankings, assessments, automated interviews, predictive models, and HR analytics—and to support meaningful oversight within the governance program by maintaining the inventory’s currency through defined change-management triggers.

For each tool, consider recording the core facts of use, impact, data, and ownership, and relying on a single inventory as the backbone for audits, governance, vendor oversight, incident response, and regulatory disclosures.

Setting Clear Rules

Setting clear rules means writing down and enforcing plain-language policies for the use of AI. That includes providing for notice (and consent where applicable), meaningful human review and appeal, data minimization and retention, and security, and making sure vendor contracts protect the organization’s legal risks with regard to audit rights, data access, security parameters, explainability documentation, and remediation obligations if problems are found.

Monitoring and Fixing

Effective systems risk management may require ongoing monitoring at set intervals, with some laws mandating periodic reviews. Consider defining clear thresholds and triggers for when to retest and remediate, and preparing a response playbook covering feature or cutoff changes, deployment adjustments, reviewer retraining, and vendor recalibration. Legal may want to continue managing the process so that analytic iterations and corrective actions remain under privilege.

Documenting to Defend

Keeping contemporaneous records that demonstrate job-relatedness, business necessity, and, where adverse impact appears, any evaluation of less discriminatory alternatives, while preserving the who/what/why of human reviews (including reasons for following or overriding AI outputs) with clear, plain-language explanations of how each tool works, is critical. Robust, contemporaneous documentation can significantly enhance a program’s defensibility in regulatory inquiries and litigation.

Next Steps

Auditing AI for bias in employment decision-making is now a critical part of AI governance and risk management. Employers that implement privileged audits, robust governance, and continuous monitoring paired with transparency, human-in-the-loop controls, and disciplined documentation can harness AI’s benefits while reducing the risk of discriminatory outcomes and regulatory exposure.

Ogletree Deakins’ Cybersecurity and Privacy, Government Contracting and Reporting, Technology, and Workforce Analytics and Compliance Practice Groups will continue to monitor developments and will provide updates on the Cybersecurity and Privacy, Employment Law, Government Contracting and Reporting, Multistate Compliance, State Developments, Technology, and Workforce Analytics and Compliance blogs.

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