Practical steps: Nurturing a healthy AI-compliance relationship

As the regulatory landscape matures, employers must ensure they proactively manage and mitigate AI-related discrimination risks. Here are the emerging best practices:

1. Inventory and assess your AI ecosystem.

Map AI use cases across the employment lifecycle: recruiting, screening, interviews, promotions, performance assessment, compensation determinations, and terminations. You can’t manage risks you haven’t identified. Understand where algorithms touch employment decisions.

2. Implement human review and escalation paths. 

Ensure meaningful human oversight around adverse decisions. “Meaningful” means the reviewer understands how the system works, has the authority to override the algorithm, and can evaluate whether outcomes align with the organisation’s values and legal obligations. Rubber-stamping algorithmic recommendations doesn’t satisfy this requirement.

3. Validate tools for adverse impact. 

Conduct regular testing to identify whether AI systems disproportionately affect protected groups. Keep current documentation of validation efforts, including the methodology used, results obtained, and any corrective actions taken. This documentation serves both compliance and defence functions.

4. Refine Americans with Disabilities Act (ADA) accommodation processes for AI assessments. 

Build clear pathways for candidates and employees to request accommodations for AI-driven evaluations. This may include alternate assessment formats, the ability to bypass certain algorithmic screening, or guaranteed human review.

5. Plan for regulatory fragmentation. 

Multi-state employers should anticipate an increasingly complex compliance landscape. What works in one jurisdiction may fall short in another. Consider whether to adopt the most stringent standard across your operations or maintain jurisdiction-specific protocols. Both approaches carry trade-offs in complexity and risk.

6. Scrutinise vendor contracts. 

Review vendor agreements to ensure transparency regarding algorithmic function, compliance with anti-discrimination laws, and appropriate risk allocation. Key provisions should address data usage and retention, audit rights, indemnification for discriminatory outcomes, data privacy, compliance with applicable laws, and vendor obligations to provide information necessary for your compliance efforts. 

Don’t accept vendor assurances of “fairness” or “compliance” at face value. Request documentation of validation studies, information about the data used to train models, and explanations of how the system reaches decisions.

AI offers genuine benefits in efficiency, consistency, and scalability across the employment lifecycle. But, like any powerful tool, it carries risks that require active management. For employers, the strategy is clear: embrace AI’s potential while building robust compliance frameworks that mitigate risks associated with discrimination claims. 

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Deja M. Davis is an associate in LP’s Employment & Executive Compensation Practice Group. She advises employers on a wide range of employment matters, enabling them to minimise risk and focus on running their businesses.