AI is being integrated into many HR functions, from scheduling and recruiting to performance management and compensation decisions. While HR processes are not fully automated, the growing use of AI in these areas can create legal and compliance risks for employers.

The use of AI may also create risks related to independent contractor (IC) classification. Because control is a key factor in determining whether a worker is an independent contractor or an employee, AI-driven management practices could blur that distinction. For example, AI tools can be used to schedule contractors, evaluate their performance, assign work, and influence compensation decisions. When these tools exert a significant degree of control over how, when, and where work is performed, regulators may conclude that the worker functions more like an employee than an independent contractor. If a contractor is reclassified as an employee, the organization could become responsible for payroll taxes, workers' compensation coverage, unemployment insurance, FMLA obligations, paid leave requirements, and other employment-related benefits and protections.

"Because control is an important consideration in worker-classification analyses, extensive AI-driven oversight may support a finding that a worker is an employee rather than an independent contractor," worker-side attorney Alison Breiter of Farah & Farah said.  With the Department of Labor updating its IC rules, it should be noted that AI is not identified as a factor yet. With comments coming in on the proposed rule, it is likely that some guidance will be forthcoming. If not, these issues will be decided by the courts.

The role AI plays in the working relationship may also influence whether a worker is classified as an independent contractor or an employee. When an independent contractor independently selects and uses AI tools to complete work, it can support the argument that the worker is operating as an independent business. In contrast, when a company provides or requires the use of AI tools that direct, schedule, or manage the contractor's work, the relationship may begin to resemble employment.

As Paul DeCamp, former Administrator of the U.S. Department of Labor's Wage and Hour Division and a member of management-side law firm Epstein Becker Green, noted, an independent contractor who chooses to use AI tools to organize tasks or manage logistics may still maintain the autonomy expected of an independent contractor. However, when a company uses AI to plan, assign, or direct work, the worker may lose the independence and decision-making authority that are hallmarks of contractor status.

AI is also increasingly being used to support compensation decisions, including wage structures, pay ranges, and pricing models. This practice is drawing heightened regulatory scrutiny. States including California, Colorado, Georgia, Illinois, and Texas have enacted or are considering laws that place limits on the use of AI in employment-related decision-making. While the specifics vary by jurisdiction, many define "automated decision systems" broadly to include software, algorithms, machine learning models, and AI-powered tools that assist with or replace human decision-making. These systems can range from simple rule-based applications to advanced generative AI technologies.

To reduce legal risk and comply with emerging regulations, employers should follow several widely recognized safeguards, including:

(1) offer individualized wages based on data related to services workers perform;

(2) disclose in plain language their use of automated decision systems, including the data considered by the systems and how the systems consider such data, to employees and applicants whose compensation is influenced or determined by these methods; and  

(3) develop and implement procedures to ensure the accuracy of the data considered by automated decision systems in setting wages.

The key compliance requirement is ensuring that applicants and employees are notified when AI tools are used in employment-related decisions.

In the area of compensation, some studies suggest that AI-driven models may recommend wages that differ from those established through traditional human decision-making processes. While AI systems can identify patterns and relationships within large datasets, the factors influencing their recommendations are not always transparent or easily understood.

This lack of transparency creates potential compliance concerns. If an AI model directly or indirectly relies on protected characteristics, such as age, race, gender, or other legally protected traits, when generating compensation recommendations, employers could face discrimination claims and regulatory scrutiny. As a result, organizations that use AI to support pay decisions should carefully evaluate and monitor these systems to ensure compliance with applicable equal pay and anti-discrimination laws.

A recent McGill University study examining AI-generated wage recommendations for freelancers found a strong correlation between compensation and age, even after efforts were made to reduce bias. Notably, the model did not justify higher pay recommendations based on experience, tenure, or other job-related factors. In some cases, compensation disparities persisted even when prompts specifically instructed the model to ignore age or assume that candidates had equivalent experience.

These findings highlight potential legal risks for employers using AI to support compensation decisions. For example, if an organization relies on AI to determine starting pay and the system recommends a lower salary for a younger minority woman than for a similarly qualified older white male employee, the employer could face claims under Title VII of the Civil Rights Act and the Equal Pay Act. It is important to note that the Equal Pay Act requires only a single comparator to establish a potential claim. Conversely, if an older applicant receives a higher salary recommendation than similarly situated younger employees, age discrimination concerns could also arise.

Geographic data presents another potential challenge. Large language models and other AI systems often consider location when generating compensation recommendations, particularly for positions that can be performed in multiple regions. While geographic differences may be a legitimate factor in compensation decisions, employers that maintain a national pay structure could inadvertently create significant pay disparities if AI-generated recommendations are not carefully reviewed.

Among HR applications, AI-generated job descriptions generally present lower legal risk when appropriate human oversight is maintained. However, organizations should ensure that managers and employees familiar with the role review AI-generated job descriptions for accuracy, completeness, and consistency with actual job duties.

The broader takeaway for HR leaders is that human oversight remains essential whenever AI is used in employment-related decision-making. Organizations should establish clear review processes, maintain transparency with applicants and employees regarding the use of AI, and understand that AI extends beyond generative tools such as large language models. Employers should work with legal counsel to assess their use of AI and ensure compliance with applicable federal, state, and local requirements.

Sources: LAW360 6/3/26, The National Law Review 1/27/26, SIXR30 10/15/26