How Employers Can Manage Risk When Using AI for Employee Performance Management
Artificial intelligence is increasingly being used by employers to support employee performance management. While AI has the potential to improve talent matching and expand opportunities for growth, it raises significant legal and compliance considerations that employers must take into account before deploying. This Insight will provide an overview of the ways in which you can use AI for performance management, summarize the inherent risks, and provide a list of steps you can take to address that risk.
How AI Performance Management Can Boost Your Workplace
Qualified employees frequently self-select out of roles they could succeed in because they are scared away by the overly long and all-encompassing job descriptions listing excessive qualifications they don’t believe they can meet. Still others pursue positions misaligned with their actual capabilities, creating frustration for employer and employee alike.
AI-driven skills analysis offers a different approach. Instead of focusing on job titles or formal credentials, predictive analytics can identify employees in the workforce with transferable skills and knowledge for job openings.
These AI-driven systems are often powered by large datasets based on employee career histories. By analyzing how employees progressed over time, what skills they developed, and what training preceded advancement, AI can generate data-informed roadmaps for internal mobility, employee upskilling, and long-term career planning.
Promotion models can also increase efforts to diversify the workforce surfacing talent from non-traditional career paths that may otherwise have been overlooked if focus was only on titles and credentials.
For example:
- Customer service representatives may be identified for HR roles based on conflict-resolution skills.
- Retail supervisors may be tapped for project management based on scheduling and coordination experience.
- Flight attendants may be flagged for compliance roles due to their regulatory training.
Where Risk Emerges
Despite their promise, AI tools used for performance management, promotion recommendations, and skill inference present risk under employment and anti-discrimination laws. Many of these risks stem from how models are trained, what data they rely on, and how outputs are used in practice. While AI tools can improve predictive accuracy, the value of these tools, like all data-driven models, depends heavily on the quality, representativeness, and explainability of the underlying data.
What Data is Used to Define Success?
A common issue is the use of historical data to define “successful” employees or ideal career paths. These datasets may reflect past inequities, including biased performance evaluations, unequal access to development opportunities, and historic underrepresentation of certain groups in senior roles. Without careful safeguards, AI systems may learn and replicate these patterns rather than correct them.
What Inferences is AI Making?
Risk can also arise when AI-driven tools infer skills based on generic inputs. For example, job titles, career gaps, performance reviews, and project assignments may unintentionally encode information correlated with sex, race, age, disability, or socioeconomic background. Even when protected characteristics are not explicitly used, these proxies can influence outcomes in ways that disadvantage certain groups.
Is AI Making Key Determinations?
Even when framed as “recommendations,” AI-driven systems can shape access to opportunities by determining which employees are presented with advancement paths, which are steered toward certain roles, and which receive high-visibility training. Employers who are self-regulating should increasingly view promotion access and development opportunities as areas of heightened scrutiny because of their direct impact on pay progression and career trajectory.
Are Opportunity Gaps Emerging?
Bias can also emerge in training and upskilling recommendations. AI systems may unevenly distribute high-value development opportunities, favor employees with greater schedule flexibility or financial resources, or nudge certain groups toward slower-advancing tracks. Over time, this can widen skill and opportunity gaps across the workforce.
Are Biases Being Amplified?
Performance review data poses its own challenges. Reviews are often influenced by rater bias, inconsistent managerial standards, halo and horn effects, and cultural communication differences. When these subjective inputs are heavily weighted, AI tools may amplify existing bias rather than mitigate it.
Are AI Decisions Explained?
Transparency is another recurring issue. Many performance and talent platforms rely on proprietary models, embeddings, or opaque skills ontologies that make it difficult to explain why certain employees are surfaced or excluded from opportunities. Lack of explainability itself increases regulatory and litigation exposure.
Are You Regularly Checking In?
Finally, some risks emerge only over time. Differences in employee engagement with AI tools, limited digital footprints for lower-visibility roles, and accessibility barriers for employees with disabilities can all lead to unequal outcomes if systems are not continuously monitored.
Managing Risk While Capturing Value: Practical Steps to Consider
Using AI for improving employee performance management requires deliberate governance. You should validate models before deploying them through bias and subgroup testing, using carefully curated and de-biased data, and not treating historical promotion patterns as ground truth. You should standardize inputs and workflows so employees are evaluated consistently, with accommodations built in where needed.
Equally important is maintaining human oversight. HR and leadership teams should review AI-generated recommendations, retain authority to override outputs, and document decision-making. Ongoing monitoring after deployment is essential to detect disparate impact, data imbalances, and drift as the workforce and inputs evolve.
Conclusion
We will continue to monitor developments related to AI performance management tools. Make sure you are subscribed to Fisher Phillips’ Insight System to get the most up-to-date information. If you have questions about your organization’s use of AI in the workplace, contact your Fisher Phillips attorney, the authors of this Insight, or any attorney in our AI, Data, and Analytics Practice Group.
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