Recruiting intelligence
Writing on inbound resume screening, EEOC adverse impact, structured hiring, and what human-in-the-loop actually means in practice — from a team that has run TA pipelines and built screening tools.
What AI Resume Screening Gets Wrong About Bias (And What Actually Helps)
Most adverse impact risks in AI screening come from proxy signals the model infers — not the job requirements it reads. Here's what changes when you strip the model down to criteria-only scoring.
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Time-to-Fill Benchmarks for 2025: What TA Teams Are Actually Measuring
Across industries, the metric that moves recruiter efficiency most isn't time-to-hire — it's time-to-first-screen-call. Where time-to-fill benchmarks actually stand in 2025, and what TA teams are doing about it.
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What Recruiters Actually Want From AI (It's Not What You'd Expect)
Conversations with 20 recruiters on what they actually want from screening tools. The most common request wasn't higher accuracy — it was being able to explain the output to a skeptical hiring manager.
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Structured Hiring and Explainability: Why TA Teams Are Rethinking the Resume Review
Structured hiring research shows that consistent criteria application outperforms intuition. But the tooling to support it in high-volume environments has lagged — until now.
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The Resume Volume Problem: Why More Applications Doesn't Mean Better Candidates
Easy-apply has created a paradox: more applicants per req, no more signal. The tools that actually help TA teams aren't the ones that reject faster — they're the ones that read the full stack before filtering.
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Human-in-the-Loop Isn't a Compromise — It's the Design
The most defensible AI recruiting tools aren't the ones that automate the most. They're the ones that give recruiters better information to make their own calls.
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