Redefines nudging, exploring its risks in AI-driven environments and suggesting governance mechanisms.
This paper redefines nudging not merely as a form of choice architecture, but as a mechanism of epistemic gradient control: the environmental shaping of attention, perception, comparison, refusal, reconsideration, and contestation before a choice is made. The paper argues that, in the age of AI, nudges can shift from static behavioural interventions into dynamic, personalized systems that adjust to an individual’s psychological state, behavioural history, vulnerability, and response patterns. Under such conditions, a subject may feel free while the practical conditions of autonomy are externally structured. To evaluate this risk, the paper proposes two scoring models: the Nudge Risk Score (NRS), based on eight dimensions—transparency, refusability, purpose alignment, information asymmetry, vulnerability exploitation, deliberation space, AI personalization, and monitorability—and the Final Nudge Risk Score (FNRS), which adjusts risk through AI amplification, vulnerability, and domain coefficients. The phrase “painless slavery” is used as a theoretical metaphor, not as an equation with historical slavery. It refers to a condition in which the subject experiences no overt coercion, pain, command, or prohibition, yet the environmental conditions of personal agency are systematically appropriated. The paper further connects this analysis to FCL-S-style epistemic stabilization and proposes practical governance mechanisms such as nudge detection warnings, non-personalized modes, audit logs, and anti-nudge interfaces.
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Hiroko Konishi (2026) studied this question.
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