HR professionals seeking to fill positions on tight timelines have been increasingly leveraging AI-powered tools to complete tasks such as screening résumés, evaluating video interviews and ranking candidates. AI offers speed, consistency and efficiency—attractive qualities when faced with filling vital roles quickly. In fact, according to a 2025 survey by HRTech Outlook, nearly 80% of companies that used AI in talent acquisition saw significant reductions in time-to-hire.
But what sacrifices are we making in favor of efficiency?
The Problem With AI And Pattern Recognition
AI models tend to rely on historical data and keywords as their sole criteria for determining low-fit candidates. Unfortunately, this can perpetuate hiring biases by screening out people from underrepresented backgrounds whose profiles don’t align with established patterns.
Think back to the controversy around Amazon’s AI hiring tool, which was deprioritizing résumés containing the qualifier “women’s.” Because the tool’s training data favored male candidates, its recommendations amplified an already biased system.
AI software was built to recognize patterns, yet it has difficulty with nuance and interpretation. It can’t understand the narrative behind résumés—the stories about challenges faced, reasons behind a gap in employment or personal growth demonstrated. Algorithms can’t distinguish among resilient veterans shifting into civilian roles, neurodivergent applicants who find traditional interviews daunting or creative personalities who pursue unconventional projects. Nor can it recognize these subtleties of personality.
Human judgment remains invaluable when leveraging automated systems. According to Brookings’ 2025 report “Breaking the AI Mirror,” human oversight can mitigate biases and ensure fairness within these platforms. When it comes to hiring, HR professionals need to play an active role in selecting tools, shaping the criteria used and challenging assumptions made within them.
The Real Opportunity In Partnering With AI Solutions
According to SHRM’s coverage on AI ethics and accountability, adopters of AI technologies should approach implementation with transparency, ethics and accountability as priority goals. Here are several strategies HR leaders can employ to ensure AI serves to amplify, not dilute, the full truth about candidates.
• Use human-in-the-loop design. As AI scans for patterns and red flags on résumés, a real human reviewer should review the ones that get flagged to catch false negatives and account for lived experience or nontraditional credentials. This dual-layer approach ensures accurate judgment when reviewing résumés submitted via AI systems.
• Ask about training data. Bias often results from AI being trained on biased datasets. When considering potential vendors, inquire about whether their tool was trained on diverse datasets. For example, does it take career breaks and self-taught skills into account? How well does it handle language patterns across different communities?
• Use AI as an additive tool. AI should inform decisions, not replace judgment. Leverage it as a tool for trend identification, résumé summarization or passive candidate identification, but retain final screening and interviews for human professionals.
• Employ empathy-informed KPIs. Train hiring teams not just on how AI tools should be utilized, but why. Establish KPIs that measure inclusive outcomes (e.g., increases in hires from underrepresented groups or nontraditional pathways) alongside efficiency metrics like time-to-hire.
• Audit results periodically. By monitoring AI use proactively, you can avoid potential claims of discrimination. You must assess who’s being recommended or excluded through AI decisions, as well as determine whether biases are being reinforced.
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HR professionals seeking to fill positions on tight timelines have been increasingly leveraging AI-powered tools to complete tasks