Using AI To Make Employment Decisions: Common Pitfalls & Legal Compliance

Artificial Intelligence has rapidly become part of the modern workplace. Employers are using AI-powered tools to screen resumes, rank candidates, summarize interviews, monitor productivity, draft performance evaluations, and even recommend discipline or termination decisions. As regulators catch up to the reality of AI in the workplace, the legal landscape governing AI in hiring, promotion, and other employment decisions is changing in real time AI creates for employers an active, expanding compliance obligation, and the rules differ significantly depending on where your candidates and employees are located. So, while these tools can be very helpful in streamlining processes and increasing efficiency, they also present significant legal risks if used without proper oversight.

AI is a Tool, Not a Decision Maker

The most effective use of AI is to support human decision making, not replace it. Although AI can quickly identify trends, organize information, and summarize large amounts of data, employment decisions should ultimately be made by trained individuals who understand the employee, the workplace, and the applicable legal standards. One of the most significant concerns surrounding AI is algorithmic bias.

Title VII, the ADA, and their state-law counterparts apply to AI-assisted decisions. Because AI biases are often hidden within complex algorithms, employers may not realize discriminatory outcomes are occurring until challenged by an applicant, employee, or government agency. An employer does not get a pass on disparate-impact liability because a machine did the sorting instead of a person. If a resume-screening tool systematically down-ranks candidates from a protected class, or a video-interview tool’s scoring correlates with a disability, the employer is exposed the same way it would be if a human recruiter did it — arguably more so, because the pattern is baked into a repeatable, auditable process rather than scattered across individual judgment calls.

Layered on top of ordinary discrimination law is a fast-growing set of state and local statutes aimed specifically at automated decision-making. Most recently, New York, Illinois, and Colorado have enacted legislation aimed at employer disclosure of the use of AI and requiring frequent, periodic audits of its AI tools. More than a dozen other states have introduced similar legislation, which is making its way through the state legislative processes.

Legal Compliance and Best Practices

Given how quickly this area is morphing, a workable compliance approach looks less like “comply with law X” and more like building infrastructure that can flex across jurisdictions. Below is a non-exhaustive list of practical steps and best practices to ensure compliance:

1. Inventory every AI tool that is used in employment decisions. Identify every system that influences employment decisions, including tools used by human resources, managers, recruiters, and third-party vendors. Understanding where AI is being used is the first step toward managing legal risk.
2. Identify whether AI is making or influencing employment decisions. Not every AI tool presents the same level of risk. Consider questions like, “does the tool summarize information, or rank/recommend candidates or employees,” “does management routinely follow AI recommendations,” “is there meaningful human review before decisions are made,” and “can a manager explain the decisions without relying solely on AI’s output.” The greater the influence of AI, the greater the legal scrutiny.
3. Review vendor documentation. Employers should understand how third-party AI vendors develop and validate their products. At a minimum, request documentation addressing bias testing, accuracy rates, training data and methodology, compliance with applicable employment laws, data security and retention practices, frequency of model updates, and customer responsibilities for oversight and compliance. Do not assume that purchasing a commercial AI product shifts legal responsibility to the vendor.
4. Evaluate for disparate impact. Employers should periodically analyze whether AI assisted decisions disproportionately affect applicants or employees based on protected characteristics such as race, sex, age, disability, national origin, or other legally protected statuses. If disparities emerge, employers should investigate whether the AI tool, the underlying data, or the decision-making process requires modifications. The EEOC has guidance on employer analysis of disparate impact of AI: https://www.eeoc.gov/laws/guidance/assessing-adverse-impact-software-algorithms-and-artificial-intelligence-used; U.S. Equal Employment Opportunity Commission, Artificial Intelligence and Algorithmic Fairness Initiative, https://www.eeoc.gov/ai.
5. Confirm ADA compliance. AI should not interfere with an applicant’s or employee’s right to a reasonable accommodation. Employers should evaluate whether alternative assessment methods are available when appropriate, AI driven interview tools disadvantage individuals with disabilities, and productivity monitoring accounts for approved accommodations or protected leave.
6. Examine data privacy and security practices. AI tools often rely on large amounts of employee and applicant data. Employers should determine what information is collected, whether employees receive appropriate notices and disclosures, how long data is retained, who has access to the information, and whether confidential or proprietary information is used to train generative AI models.
7. Review internal policies. Most employee handbooks and technology policies were drafted before widespread AI adoption. Employers should consider updating policies to address acceptable use of generative AI, confidential information, employee monitoring and electronic communications, and AI governance and approval procedures.
8. Train your decision makers. Managers should understand both the capabilities and limitations of AI. Training should emphasize that AI recommendations are advisory, not mandatory, and managers remain responsible for decisions, human judgment must be exercised, and documentation reflecting the independent reasons supporting employment actions. Consider an internal policy for managers and hiring staff that delineates appropriate use of AI. For example, randomly running applicant names through AI and making decisions based on that information is not advised. Conversely, using a 3^rd party AI platform, or creating policies on using an AI platform for sorting or summarizing capabilities may be appropriate.
9. Establish ongoing monitoring. Compliance is not a one-time project. As AI systems evolve through updates, retraining, or changes in underlying data, employers should periodically reassess accuracy, bias testing, vendor performance, and internal usage patterns. Regular monitoring allows employers to identify emerging risks before they become legal problems.

Conclusion

AI has enormous potential to improve workplace efficiency, but it is not a substitute for thoughtful management or legal compliance. Employers should view AI as a decision-support tool and not the ultimate decision maker. Human judgment, documented reasoning, and consistent application of employment policies remain essential to reducing legal risk. As AI continues to evolve, so will the legal landscape. Employers that adopt AI thoughtfully, monitor its outcomes, and maintain appropriate human oversight will be better positioned to realize its benefits while minimizing exposure to discrimination, privacy, and employment-related claims. Employers using AI anywhere in the employment lifecycle should treat this as an ongoing governance function, not a project with an end date.