Shame in the machine: affective accountability and the ethics of AI

Shame in the machine: affective accountability and the ethics of AI

연구 분야: Artificial Intelligence

학회: AI & SOCIETY

The cultural weaponization of shame surrounding the use of artificial intelligence (AI) tools like ChatGPT often redirects ethical scrutiny away from systemic concerns and toward individual users. Drawing on Sara Ahmed’s affect theory, this paper argues that cultural narratives of "AI shaming" function as moral displacement that redirects scrutiny away from the environmental costs, exploitative labor practices, and corporate monopolization defining contemporary AI development. The analysis examines how shame operates across academic and professional settings to create "effort anxiety" that demands both visible human labor and accelerated productivity. Current discourse treats AI use as a personal virtue problem and obscures the carbon-intensive data centers, underpaid content moderators, and proprietary knowledge systems that enable these technologies. Instead of eliminating shame, the paper proposes redirecting it toward collective accountability for AI’s systemic harms. Environmental degradation, algorithmic bias, and extractive infrastructures represent the true ethical frontier of artificial intelligence. Policy frameworks, educational interventions, and governance structures offer pathways for transforming shame from individual punishment into institutional reform. The stakes extend beyond AI itself: as emerging technologies reshape society, the patterns of moral responsibility established now will determine whether innovation serves collective flourishing or perpetuates existing inequalities. Shame can become a vehicle for institutional critique and systemic accountability if we redirect its focus from individual users to the powerful corporations, governance structures, and infrastructural systems that profit from AI’s rapid expansion.