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Companies should use AI-powered tools to make hiring decisions.
AI can automate early screening and scheduling, cutting time-to-hire. Unilever reported saving ~100,000 recruiter hours using AI-assisted video assessments.
AI can enforce structured, standardized evaluations—an approach linked to lower bias and more reliable hiring decisions.
Skills-based, AI-enabled matching broadens candidate pools beyond degrees. LinkedIn reports sizeable expansions globally, with larger gains for non-degree workers.
AI can inherit historical bias: Amazon scrapped an experimental recruiting engine after it downgraded resumes associated with women.
Opacity is a risk; New York City now requires annual bias audits and candidate notices for automated employment decision tools.
Automation may undervalue human, context-dependent qualities (e.g., empathy and judgment) that employers still prize.
Debate over AI in hiring weighs efficiency and scale against risks of bias and opacity. Proponents cite faster cycles, standardized evaluations, and skills-based matching; critics warn of learned bias, black-box decisions, and the loss of human-centered judgment.
AI entered recruitment in the 2010s and grew rapidly in the 2020s. High-profile failures (e.g., Amazon’s scrapped tool) prompted regulatory responses like New York City’s bias-audit requirement, while firms experiment with structured and skills-based approaches.