10 Practical Steps Employers Should Take to Mitigate AI Bias and Manage Workplace Risk
Artificial intelligence has become increasingly embedded in hiring, promotion, and employee management, which means that employers face heightened legal risks. From automated résumé screening to video interview tools and performance analytics, AI tools can amplify bias, create disparate impact, and expose organizations to regulatory scrutiny. Below are 10 practical steps you should consider to mitigate bias and manage risk throughout the AI employment lifecycle.
1. Validate Before You Deploy
Before rolling out any AI tool, conduct rigorous pre-deployment testing. This includes bias and disparate-impact audits across protected groups (race, sex, age, disability) and job categories. Require vendors to provide documentation of their own testing, accuracy data, and bias audit results. Don’t assume a high statistical correlation between a model’s features and job performance means the tool is job-relevant. Always ask if the features logically relate to actual job duties.
2. Monitor Outcomes Over Time
Bias mitigation is not a one-time event. You should track demographic and performance data after hiring or promotion decisions. If patterns of bias or disparate impact emerge, adjust or retrain the model. Regular post-deployment audits are essential to catch “drift” as new data enters the system.
3. Establish Strong Governance
Implement clear policies for AI use, including documentation of all testing, audits, and remediation steps. Maintain records of how decisions are made, which features are used, and how human oversight is integrated. This documentation is critical for regulatory compliance and defending decisions if challenged.
4. Know the Model’s Features and Filters
Demand transparency from vendors about which résumé factors or data points the model uses and which disqualify candidates. Understand the “disqualifying” features and ensure they are job-related and non-discriminatory.
5. Avoid Bloated Job Descriptions
Overly broad job postings that mix “must-haves” with “nice-to-haves” create data noise, making it harder for AI to identify true qualifications. This can lead to models weighing irrelevant factors (like education pedigree or résumé formatting), amplifying bias. The solution is to provide cleaner, more focused job data.
6. Strengthen Vendor Due Diligence
Vet vendors thoroughly. Require contractual assurances on data quality, explainability, audit access, and strict limits on data use and retention. Ensure vendors comply with privacy, notice, and consent requirements, especially when tools capture biometric-like data (e.g., voice, facial movement).
7. Comply with Emerging Regulations
Determine if the tool qualifies as an automated decision tool (ADT) under laws like NYC Local Law 144, California’s pending ADMT regulations, Illinois’s AI Video Interview Act, or Colorado’s upcoming law. Complete required audits, notices, and candidate disclosures.
8. Maintain Human Oversight
AI should inform – not replace – human judgment. Someone in your hiring loop should review AI-generated scores or recommendations and retain authority to override automated decisions. Document when and why human intervention occurs.
9. Standardize and Accommodate
For tools like AI video interviews, standardize the experience: same questions, prompts, and instructions for all candidates. Provide technical guidance and offer accommodations for disabilities to avoid ADA risks.
10. Ensure Candidate Transparency
Disclose AI use to candidates and offer non-AI alternatives when possible. Transparency builds trust and is increasingly required by law.
Conclusion
We will continue to monitor developments related to AI hiring 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 recruiting or hiring, 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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