AI Shaming in Organizations: When Technology Adoption Threatens Professional Identity, by Jonathan H. Westover PhD

Episode notes

Abstract: Recent field-experimental evidence reveals that workers systematically reduce their reliance on artificial intelligence recommendations when that usage is visible to evaluators, even at measurable performance costs. This phenomenon—termed "AI shaming"—reflects emerging workplace norms in which heavy AI adoption signals lack of confidence, competence, or independent judgment. Drawing on labor economics, organizational behavior, and technology adoption research, this article examines how image concerns shape AI integration in contemporary organizations. Analysis shows that workers fear visible AI reliance conveys weakness in judgment—a trait increasingly valued in AI-assisted work—leading to systematic under-utilization of algorithmic recommendations. The performance penalty is substantial: accuracy declines approximately 3.4% when AI use becomes observable, with one in four potential successful human-AI collaborations lost to visibility concerns. These effects persist despite explicit performance incentives, reassurances about worker quality, and clear communication that evaluators assess only accuracy on identical AI-assisted tasks.