7 Best Practices for Employers Using AI Resume Screeners

Nearly all Fortune 500 companies currently use algorithmic tools during their hiring process, and AI-driven resume screeners are especially common. These tools promise speed, consistency, and scalability, but they also bring significant legal, ethical, and operational risks. Employers that rely on AI resume screeners must understand how these tools work, recognize the potential risks, and manage them responsibly. Here’s an overview about the use of this technology along with a list of seven best practices for employers.

Resume Screeners Have Obvious Appeal to Employers

The appeal of AI screening is clear. Large employers often receive an unmanageable number of applications, sometimes millions each year, and even a single job posting can draw hundreds or thousands of candidates. It is not feasible for recruiting teams to manually review every resume, and in many cases, resumes receive only a few seconds of attention (if they are reviewed at all). As a result, many employers have turned to AI to help narrow the field.

Practical Challenges of AI Resume Screening

However, efficiency has its tradeoffs.

• AI systems can filter out strong candidates simply because their resumes do not match the exact phrasing of a job description.
• Qualified applicants may also be overlooked for using different terminology, structuring their experience in an unconventional way, or highlighting skills the model does not prioritize.
• AI also struggles to assess qualities that are critical in hiring but difficult to quantify, such as communication skills, adaptability, and leadership potential.
• Candidates with non-traditional backgrounds or unconventional career paths can also be disproportionately disadvantaged.

Candidate Behavior and the Escalating “Arms Race”

As employers adopt AI tools, candidates are adapting just as quickly. Many now use generative AI to tailor their resumes to match job descriptions, which has led to a dramatic increase in applications. Others use autonomous AI agents to mass apply to jobs or embed hidden prompts intended to influence screening algorithms. The surge in AI-generated resumes means that 40% to 80% of applicants may now be using these tools, resulting in a flood of nearly identical, keyword-heavy documents.

This makes it harder to distinguish genuinely qualified candidates from those who have simply mastered prompt engineering. Some applicants even attempt to manipulate screening tools by embedding hidden instructions in white text, forcing employers to update their systems and, in some cases, automatically reject manipulated submissions.

This evolving dynamic has created a technological arms race. Candidates optimize their application and resume to get through AI filters, while employers respond by tightening their screening criteria or deploying detection tools. The result is a more complex and less predictable hiring environment that demands careful oversight to avoid unintended consequences.

Recognize the Potential for Bias

Most AI resume screeners are trained on historical hiring data. They analyze the characteristics of past successful applicants, such as job titles, education, skills, and keywords, and assign weights to these features. Candidates are then scored or ranked based on how closely they match these patterns.

The problem is that historical data often reflects the biases of previous decision-makers. For example, if a company’s engineering workforce has been predominantly male, the model may learn to favor male-associated signals, even if gender is not explicitly included. Bias can also creep in through seemingly neutral features like extracurricular activities, writing style, educational background, or geographic indicators.

Attempts to remove protected characteristics are not always effective. AI can infer gender, race, or socioeconomic status from names, schools, neighborhoods, or even word choice. There have been high-profile situations where AI recruiting tools were found to downgrade resumes from certain groups, such as graduates of all-women’s colleges. Studies have also shown that resumes with White-associated names are more likely to be favored than those with Black-associated names, even when qualifications are identical.

Legal and Compliance Considerations

Employers generally cannot outsource liability. Government regulators and courts have made it clear that existing anti-discrimination laws apply equally to AI-driven and human-driven selection procedures. Some jurisdictions now require audits or disclosures for AI hiring tools, and more regulation is expected. Organizations that use AI in hiring must be able to show that their tools are job-related, consistently applied, and do not result in unlawful disparate treatment or impact.

Best Practices for Employers Using AI Resume Screeners

1. Understand and Document Model Features: Know what the AI is evaluating and how those features influence candidate rankings. Require transparency from vendors and keep thorough internal documentation.
2. Conduct Regular Bias Audits: Test for disparate impact across protected groups and job categories. Adjust or retrain the model promptly if bias is detected. Learn more here.
3. Monitor Outcomes Over Time: Track demographic and performance data after hiring to identify and address emerging disparities and intervene early.
4. Maintain Human Oversight: Use AI to inform, not replace, human judgment. Ensure that borderline cases or unusual profiles receive human review, especially when AI outputs correlate with protected characteristics.
5. Use Clean, Job-Relevant Data: Avoid overly broad or inflated job descriptions. Mixing essential and non-essential criteria can confuse both AI and human reviewers and increase reliance on secondary, potentially biased signals.
6. Be Transparent with Candidates: Explain how AI is used, what data is collected, and the extent of human involvement. Transparency builds trust and helps meet evolving regulatory expectations.
7. Train HR Staff: Ensure HR professionals understand AI’s strengths, limitations, and the importance of bias mitigation.

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, our Privacy and Cyber Practice Group, or our FCRA and Background Screening Practice Group.