AI In Workforce Decisions: Privilege Strategies And Policy Guardrails Every Employer Needs

A recent lawsuit filed in the Northern District of California alleges that AI-based tools used to score, rank, and select employees for a reduction in force discriminated against workers on protected leave and those with disabilities. The lawsuit alleges this occurred, in part, because the AI-system’s inputs, such as productivity metrics and “AI-native” ratings, could not be accumulated by employees who were absent or working under various accommodations. The complaint further asserts that, based on information and belief, the employer did not neutralize those inputs for protected leave; did not exclude protected-leave-takers or accommodation-seekers from the selection cohort; and did not pause the system for the individualized, leave- and accommodation-neutral review that the law requires.

This lawsuit serves as a timely reminder for employers considering, or already deploying, AI tools in layoffs, performance management, or other high-stakes employment decisions—specifically, underscoring the inherent legal risks of developing technology. As we have explored in our prior publications addressing AI, privilege, confidentiality, and the issues every business must address in its first AI policy, two specific areas demand immediate attention: (1) how companies structure AI-related reviews to protect privilege and confidentiality, and (2) what internal governance frameworks are in place before an employee hits “enter” on the AI platform.

Structure AI Assessments to Maximize Privilege

One of the most significant and frequently overlooked risks of using AI in employment decisions is the discoverability of the analysis behind those decisions. Employers routinely conduct bias audits, adverse impact analyses, and vendor assessments of AI tools, but if those analyses are not structured properly, they can become plaintiff’s “Exhibit A.” (For a deeper treatment of the privilege landscape as it applies to AI, see our recent article on AI, Privilege, and the Future of Confidentiality in the Workplace and Beyond.)

Here is how to get it right:

• Engage outside counsel from the start. The most effective step an employer can take is to ensure that any review of an AI tool’s design, inputs, or outputs is initiated by or conducted at the direction of legal counsel. An audit commissioned by HR or a business unit, without counsel’s involvement, is far more likely to be deemed a routine business record than a privileged communication.
• Define the purpose in writing. When retaining outside consultants, data scientists, or vendors to evaluate an AI tool, the engagement letter should make clear that the work is being performed to assist counsel in providing legal advice. Avoid framing the engagement as a general “best practices” review or a compliance check as those characterizations can undermine privilege claims.
• Separate privileged and non-privileged workstreams. Not every document generated in connection with an AI assessment needs to be privileged, and attempting to paint everything as such can backfire. The better approach is to maintain a clear distinction between (1) the factual, operational work of deploying and monitoring the tool and (2) the legal analysis of whether the tool’s design or outputs create litigation risk. Keep legal memoranda, risk assessments, and counsel’s recommendations in a separate, clearly marked channel. This is particularly important because, as AI tools increasingly handle sensitive data, the line between operational use and legal risk analysis can blur quickly.
• Control distribution. Privilege is waived by disclosure, so limit the circulation of privileged assessments to those with a “need to know,” and label documents appropriately. Sharing a privileged bias audit with the full C-suite (or with an outside AI vendor) can destroy the protection entirely.
• Be mindful of state-law wrinkles. Several jurisdictions with AI-governance statutes impose affirmative obligations to conduct impact assessments that may need to be disclosed to regulators or affected individuals. Counsel should evaluate whether mandatory disclosures can be structured in a way that preserves privilege over the underlying legal analysis, even where the summary results must be shared.

Build Internal Policy Guardrails Before You Deploy

Privilege protects the analysis, but it is not a compliance strategy. Employers need substantive governance frameworks in place before any AI tool touches an employment decision. (For organizations building these frameworks from the ground up, our article on Your First AI Policy: Six Burning Issues for Every Growing Business to Consider offers a practical starting point.)

• Adopt an AI-in-employment policy. At a minimum, the policy should define which employment decisions may use AI tools, require pre-deployment legal review, establish approval workflows, and assign accountability for ongoing monitoring. A policy that sits in a drawer is no policy at all, so make sure it is operationalized and that managers understand it.
• Vet your inputs. Recent lawsuits illustrate the risk of feeding facially neutral data into an AI system without asking whether that data can be generated equally by all employees. Before deploying any AI-assisted selection tool, map every input variable and assess whether it could serve as a proxy for a legally protected characteristic.
• Require human oversight. Automated scoring should inform, not replace, human judgment. Build a mandatory review layer into any AI-assisted adverse action that requires a trained decision-maker to evaluate the AI’s output in light of the employee’s individualized factors, such as leave history, accommodation status, among others.
• Establish exclusion and adjustment protocols. Determine in advance how the organization will handle employees on protected leave, those with open accommodation requests, and others whose data may suggest a legally protected characteristic. Options include excluding those employees from the AI-scored cohort, normalizing their metrics, or routing them to a separate, manual review track.
• Conduct pre-deployment adverse impact testing. Run the AI tool’s outputs against your workforce demographics before any decisions are made. If the results show a statistically significant disparate impact on a protected group, pause and evaluate with counsel whether the tool can be recalibrated or whether an alternative selection method is warranted.
• Train your people. Policies are only as good as the managers who implement them. Ensure that HR professionals, business leaders, and anyone involved in AI-assisted decision-making understands the legal framework, the organization’s policies, and when to escalate.

The Bottom Line

AI-assisted employment decisions are here to stay, and so is the litigation and regulatory scrutiny that follows them. Employers who invest now in privilege-protected assessments and robust internal governance will be far better positioned to defend their decisions if challenged. Those who do not may find themselves learning the hard way that speed and efficiency are no substitute for legal compliance.