"Till I can get my satisfaction": Open Questions in the Public Desire to Punish AI

2025-03-05

AI Summary

This paper addresses the growing public expectation—following AI-caused harm—regarding whether and how AI systems should be punished, exposing a systemic gap in existing legal and ethical frameworks to meaningfully respond to such normative demands. Method: Integrating insights from psychology, human–computer interaction, philosophy, AI ethics, and jurisprudence, the study employs critical literature review and conceptual modeling to analyze public attributions of moral agency and accountability. Contribution/Results: It introduces the novel concept of the “satisfaction gap”—the disjunction between affective public demands for punitive responses and institutional capacities for attributable, procedurally legitimate accountability. The paper clarifies the psychological drivers of anthropomorphic attribution and its attendant normative tensions, thereby filling a critical void in AI accountability research concerning public expectations. Moreover, it establishes an interdisciplinary research agenda aimed at reconciling societal legitimacy with technical feasibility, offering both theoretical grounding and actionable pathways for designing socially acceptable AI accountability mechanisms.

Technology Category

Philosophy and Ethics of AI: Accountability, Interpretability & ExplainabilityHumans and AI: Learning Human Values and PreferencesNatural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalizationResponsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

There are countless examples of how AI can cause harm, and increasing evidence that the public are willing to ascribe blame to the AI itself, regardless of how"illogical"this might seem. This raises the question of whether and how the public might expect AI to be punished for this harm. However, public expectations of the punishment of AI have been vastly underexplored. Understanding these expectations is vital, as the public may feel the lingering effect of harm unless their desire for punishment is satisfied. We synthesise research from psychology, human-computer and -robot interaction, philosophy and AI ethics, and law to highlight how our understanding of this issue is still lacking. We call for an interdisciplinary programme of research to establish how we can best satisfy victims of AI harm, for fear of creating a"satisfaction gap"where legal punishment of AI (or not) fails to meet public expectations.

Problem

• Public expectations of AI punishment are underexplored.
• Understanding public desire for AI punishment is crucial.
• Interdisciplinary research is needed to address the satisfaction gap.

Innovation

• Interdisciplinary research approach
• Public punishment expectations analysis
• Satisfaction gap identification