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In August 2025, the article “Is There Fairness in AI?” was published in the Journal of Management Studies to examine how AI-based hiring practices might lead to fairer hiring practices. The authors have now released a shorter, practitioner-focused version of that research in a new HBR article. The research draws on a three-year field study of a global consumer-goods company processing more than 10,000 job applicants per year. It examines how AI-based hiring systems support more objective hiring practices. To reduce hiring bias, the organization replaced resume reviews with blinded, gamified assessments analyzed by AI and trained the algorithm on current employees’ game data linked to performance outcomes. While this increased consistency, the study found that one definition of fairness—standardization and uniform rules—became hard-coded into the system, deprioritizing managers’ local judgment about role requirements, labor markets, and team needs. Fixed thresholds automatically advanced or rejected candidates, and deviations from the model were framed as breaking the rules rather than as exercising judgment that might otherwise account for regional talent shortages, known candidates (such as interns or project contributors) whose capabilities exceeded what assessments captured, and other contextual factors. The authors also provide questions to help organizations better leverage AI-enabled hiring tools while preserving human and contextual judgment. Both versions of the article are worth reading to understand the study’s limitations and implications.
