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What's one best practice for ensuring that pre-employment tests are unbiased and inclusive?
Deadline: May 8th, 2024 06:59 AM
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Why fragmented AI hiring stacks are failing (and what actually works) We’re writing a thought leadership article for TestGorilla titled: “The skills-first AI stack is quietly replacing traditional recruiting workflows.” Many hiring teams have adopted multiple AI tools for sourcing, resume screening, interviewing, and analytics — yet still struggle with inconsistency, bias, and poor decision-making because those tools don’t work together. We’re looking for TA leaders, HR tech leaders, People Ops, or recruiters who have firsthand experience with AI hiring tools to share practical insights on: • What breaks when hiring teams stack multiple AI tools that don’t share data or criteria • How fragmented hiring workflows impact speed, fairness, or candidate quality • Why keyword-based screening still creates blind spots — even with AI involved • What changes when teams shift to skills-based, end-to-end hiring workflows • Lessons learned from simplifying or consolidating hiring tech stacks Please reply with 2–4 sentences per question you choose, or share a short perspective drawing on your experience. Requirements: • Hands-on experience using or evaluating AI tools in hiring • Insights based on real workflows or outcomes (not theory) • Willingness to be quoted What you get: • Attribution in a TestGorilla thought leadership article read by thousands of HR and TA leaders globally • Opportunity to contribute to an industry conversation on the future of AI-driven hiring • Attribution in a dedicated Contributors section including name, title, company, and a professional profile link.