New Research on AI and Fairness in Hiring

AI promises to make hiring fairer by reducing human bias, yet it often reshapes what fairness means—and locks in one definition. A three-year study of a global consumer-goods firm found that their algorithmic system privileged a rigid definition that sidelined manager’s local judgment. This resulted in narrow candidate pools and unhappy hiring managers. The study underlined that fairness isn’t embedded in code, it’s negotiated by people who design and deploy it. To make AI hiring systems work better for more people, leaders must ask: Which versions of fairness exist? Who gives AI authority? What perspectives get lost over time? Answering these questions will help leaders treat fairness as dynamic, not fixed.