Navigating Legal Risks of AI in Employee Performance Management

As AI technology becomes increasingly embedded in employee performance management, HR processes, and customer support, companies encounter both transformative opportunities and emerging risks. In a recent webinar, legal expert Stacey Chiu, Senior Associate at Michelman & Robinson, joined Vasu Prathipati, CEO of MaestroQA, to explore these risks and opportunities. Together, they offered insights into how companies can maximize AI’s potential while protecting themselves against compliance and legal risks.

In this blog, we’ll unpack their discussion and share actionable strategies for adopting AI responsibly in employee evaluations and recruiting.

AI Bias, ADA Compliance, and Legal Risks

AI tools bring undeniable speed and efficiency to employee evaluations, but they also come with significant risks. Chief among these is a lack of contextual understanding, especially when it comes to meeting ADA (Americans with Disabilities Act) compliance standards. In Stacey’s words, while AI might speed up certain processes, it doesn’t inherently know when to adjust its calculations for employees who need special accommodations.

ADA Compliance and Bias Risks in AI Evaluations
One of the biggest risks of using AI in performance assessments is that it can overlook accommodations and nuance, leading to unintentional discrimination. For instance, an AI system might evaluate productivity by looking at keystrokes or task completion rates. But without human oversight, the system might penalize an employee with arthritis who needs regular breaks or an employee with ADHD who requires flexible working hours.

This unintentional bias can expose companies to serious legal repercussions. Stacey explained, “For someone with arthritis or ADHD, an AI system might penalize them for reduced performance without considering these factors. Without human oversight, you’re opening yourself up to significant legal risks.” This points to a clear and pressing need for companies to balance AI’s power with human review, especially in performance evaluations where ADA compliance is crucial.

Steps for Ensuring Fair and Compliant AI Evaluations
Preventing these pitfalls doesn’t require a complete overhaul—just a few straightforward actions can help companies ensure fairness and avoid bias in AI-driven assessments. Regular audits of AI performance evaluations, particularly for ADA compliance, are a good start. Companies should also ensure they have a human “check” on AI outcomes, especially in any assessment that may impact an employee’s job security, compensation, or progression.

Another key step is documentation. By keeping thorough records of how performance evaluations are conducted, companies can not only track for compliance but also provide a defensible process if discrimination claims arise. These simple practices make a significant difference in keeping AI-driven evaluations both accurate and legally sound.

Safeguarding with a "Human in the Loop" Approach

AI can be a powerful tool for identifying performance trends and flagging areas for improvement. However, leaving the final judgment to AI alone, especially in evaluations that impact real people, risks missing the context that only a human can bring. Without the perspective of a manager or team lead, AI might misinterpret performance data, leading to potential inaccuracies and, in some cases, unintentional biases.

Why Human Oversight Matters in AI Evaluations
In many cases, AI might pick up on surface-level metrics—like call completion times or task efficiency—without understanding the factors behind them. For example, an AI might flag an agent for not meeting productivity benchmarks, missing the fact that the agent was handling particularly complex cases or dealing with customer escalations. This is where “Human in the Loop” (HITL) comes into play. HITL combines the speed and scale of AI with the insight and judgment that only humans can provide. This model means AI provides the preliminary insights, while human reviewers add context, ensuring that evaluations are fair and meaningful.

A Practical Approach: MaestroQA’s Copilot Tool

To support this balance of technology and human insight, MaestroQA developed a platform for AI calibration, the Copilot feature. Copilot allows AI to highlight potential issues in employee performance while leaving the final decision to trained human reviewers. This setup not only supports compliance but also ensures that performance reviews account for individual circumstances. For instance, while Copilot can point to patterns or trends across calls or cases, it’s the human review that adds the crucial context and final validation.

As MaestroQA’s CEO, Vasu, puts it, “Use AI to tell you where to look, but let humans make the final judgment to ensure fairness.” This combination of AI-driven insights with human oversight helps organizations get the most out of AI without sidelining the human element—keeping evaluations accurate, fair, and legally sound.

For companies adopting AI in performance management, a “human in the loop” approach offers a practical solution. By involving people in the final evaluation stage, businesses can benefit from AI’s efficiency while making sure assessments align with organizational values and legal requirements.

Emerging Legal Trends in AI Use

As AI continues to play a bigger role in hiring and performance management, states are beginning to take action, implementing regulations to manage the risks AI can pose. These early moves offer a preview of the stricter oversight likely on the horizon.

Current Regulations and the Path Forward
In New York, the Bias Audit Law requires companies using AI in recruiting to conduct annual bias audits, ensuring that their AI tools aren’t discriminating against protected groups. This law applies to automated tools used to evaluate job candidates and mandates audits and transparency in the tool's selection criteria. Illinois has followed with a similar rule: companies must notify candidates when AI tools are used to evaluate video interviews. While these laws are relatively new, they’re a clear signal that AI in HR is on the radar for regulators.

‘These regulations are just the beginning. Similar laws will likely emerge across the U.S. in the coming years,’ Stacey noted, indicating that regulatory momentum is building.

The NYC Bias Audit Law is especially noteworthy as it mandates that employers not only conduct independent audits for bias but also publicly report their findings. This level of transparency may soon be the standard, urging companies nationwide to adopt similar safeguards preemptively.

The Need for Proactive Compliance
Staying on top of these evolving requirements is crucial. Even if a company isn’t directly impacted by these laws today, preparing for future compliance can save time and resources down the road. Companies that are proactive—reviewing AI implementations, working with legal advisors, and conducting internal bias audits—will be better positioned when regulations expand. A structured review process now can help companies avoid disruptions if and when nationwide regulations take effect. Companies using software for AI compliance in HR can establish these proactive checks to stay ahead of regulations.

The Role of QA Teams in AI Monitoring
QA teams play a key role in monitoring and auditing AI processes to ensure compliance. By actively reviewing AI outputs for fairness and accuracy, QA teams can prevent unintended biases before they become compliance issues. This ongoing review process not only safeguards companies from legal exposure but also builds a culture of accountability and fairness.

As AI regulation tightens, companies that take these proactive steps will be better prepared for the future, creating a strong foundation for AI adoption that respects both legal standards and ethical practices.

Proactive Compliance: Why Early Action Matters

When it comes to compliance, waiting until regulations force action is a risky approach. AI in HR and employee evaluations brings with it the potential for discrimination and privacy issues, and regulators are already starting to set their sights on these areas. Acting now—before regulations become stricter—can help companies avoid legal exposure and costly adjustments.

The Risks of Delaying Compliance
One of the key takeaways from Stacey’s insights is that waiting until something goes wrong can be disastrous. As she put it, “The legal system usually catches up when something catastrophic happens. By then, it’s too late.” Companies that delay compliance may find themselves scrambling to address regulatory demands after the fact, often at a high cost and with limited options.

Benefits of Early Compliance Initiatives
Companies can take a few practical steps today to build a defensible compliance framework. Regular bias audits, human oversight, and clear documentation are essential. By documenting AI processes, tracking how decisions are made, and having a clear audit trail, companies create a system that’s both defensible and fair.

Documentation, Stacey noted, isn’t just about having records on hand. It’s also about being ready for a possible regulatory future where companies may need to show exactly how AI systems function, especially in cases involving protected classes or accommodations. Companies can also avoid sudden disruptions by adopting a system of bias checks and audits now, making compliance more manageable if new laws come into effect.

Actionable Compliance Steps
For companies looking to stay ahead of the curve, the steps are straightforward but impactful:

1. Regularly Review and Update AI Models: Keep AI algorithms current, and test them periodically to ensure they align with compliance standards.
2. Train Employees on AI Use: Make sure employees understand how AI supports (not replaces) human judgment in evaluations.
3. Document AI-Assisted Performance Reviews: Maintain detailed records for every AI-influenced decision. This not only supports compliance but also provides transparency if questions arise.
4. Establish a structured QA process for AI compliance: Regularly review outputs and address any discrepancies early.

Staying proactive about compliance isn’t just a matter of avoiding penalties—it’s an investment in sustainable, responsible AI use that safeguards both companies and employees. By taking steps now, organizations position themselves for success in an increasingly regulated AI landscape.

AI’s Role in Performance Evaluations and Legal Compliance

AI can support HR teams in identifying performance trends, surfacing potential areas for growth, and standardizing certain elements of evaluations. However, when it comes to the final assessment, human judgment is essential to ensuring evaluations are both fair and legally compliant. AI works best as a guide—helping managers spot issues and focus their efforts on areas that may need attention—rather than as the final decision-maker in employee evaluations.

AI as a Supplement, Not a Replacement
The goal in using AI for performance evaluations should be to help HR teams make more informed decisions without replacing the human touch. For example, AI might highlight a dip in productivity, but a manager is still needed to interpret why that dip occurred—perhaps the employee was tackling a particularly complex project or had recently returned from leave. AI tools lack this kind of situational awareness, which is crucial for understanding an employee’s full performance context.

“AI’s power lies in its ability to support human judgment, not replace it. This proactive approach can prevent bias and ensure fair evaluations,” Stacey explained in the webinar, emphasizing AI’s role as a supplemental resource rather than a standalone judge.

Handling AI-Reviewed Data Responsibly
Data from AI assessments should be handled with care to avoid potential bias or misinterpretation. AI can be helpful in backing up a manager’s perspective with data, but HR teams should treat AI evaluations as just one part of the overall assessment. For instance, using AI to track call metrics in customer support can provide objective data on performance trends. However, without human oversight, these metrics alone can overlook factors like call difficulty, client satisfaction, or special accommodations for employees who may work at different paces due to health needs.

Best Practices for Ethical AI in Evaluations
Implementing AI in performance reviews with an ethical approach involves a few best practices. First, establish clear criteria for when and how AI will be used in evaluations. This includes creating guidelines for human oversight, ensuring that every AI-assisted assessment is reviewed by a manager or HR specialist before it informs any final decisions. Transparency with employees is also critical—make sure they understand that AI is a supportive tool, not a replacement for human assessment, and provide clarity around how AI data contributes to their reviews.

By implementing these best practices, companies can avoid common pitfalls associated with AI, such as unintentional bias or a lack of contextual accuracy. Done right, AI becomes a valuable tool that enhances performance management while upholding fairness and compliance with employment standards.

Want to learn more?
If you missed the live webinar, the recording is now available! Watch it here.

To explore how MaestroQA’s Copilot feature can help your team adopt AI responsibly, schedule a demo with us.