From overt deterrence to covert internalization: Moral effects of AI regulation and the moderating role of personality traits

As generative artificial intelligence (GenAI) evolves into social agents with autonomous influence, its impact on human moral decision-making is becoming increasingly significant. Current regulatory models are often grounded in the “rational person hypothesis,” which assumes uniform responses to ethical constraints. This perspective, however, overlooks the profound moderating role of personality traits in moral choices, leading to divergent regulatory effects and a loss of efficiency. The Dark Triad of personality (narcissism, Machiavellianism, and psychopathy) is a robust predictor of moral deviation. To address this gap, we constructed a “Regulation Type × Personality Trait” interaction model. We hypothesized that the effectiveness of different AI-driven intervention strategies—namely explicit regulation, implicit incentives, and moral feedback—would be significantly moderated by individuals' Dark Triad traits when making decisions about honesty. A series of experiments were conducted to test our hypotheses. The study utilized a modified coin-flip task where participants privately guessed and reported outcomes, a paradigm designed to create opportunities for dishonest behavior for personal gain. Participants' honesty rates and reaction times were recorded as the primary dependent variables. Across the experiments, we manipulated the AI-driven intervention strategies. These strategies included: (1) explicit (visible) versus implicit (invisible) AI surveillance which involved potential penalties for dishonesty; (2) implicit monetary incentives which rewarded consistent honesty; and (3) moral feedback which provided textual messages in response to honest or dishonest reports. Prior to the behavioral tasks, participants' personality traits were measured using the validated Short Dark Triad (SD3) scale. The results supported our hypotheses, demonstrating significant interactions between intervention types and personality traits. In Experiment 1, explicit AI surveillance significantly increased honest reporting (t(45) = 4.59, p t(25) = 4.60, p = 0.005) and psychopathy (t(28) = 4.44, p F(2, 90) = 18.10, p F(2, 90) = 34.10, p OR = 0.70, p = 0.013) but were more dishonest without or under invisible AI surveillance. In Experiment 3, potential financial rewards increased reaction time (F(2, 118) = 58.59, p t(57.98) = −2.04, p = 0.044. In Experiment 3a and 3b, financial incentives promoted honesty more effectively than moral messaging during the reward stage (t(120) = 3.07, p = 0.003) and maintained this effect into the internalization stage (t(120) = 2.06, p = 0.041), demonstrating the robustness of monetary influence. High Machiavellian participants sustained higher honesty levels in the internalization stage (OR = 1.96, p t(49.95) = −2.55, p = 0.013. This study was the first to systematically reveal the critical moderating role of the Dark Triad personality traits in AI ethical regulation. The findings challenge the traditional 'rational person' paradigm by empirically demonstrating the significant personality-based heterogeneity of regulatory effects. The core contribution of this research is the proposal of an innovative concept: 'personality-regulated regulation.' This framework provides a vital theoretical and practical foundation for designing future AI ethical intervention strategies that are contextualized and personalized. Such an approach allows for the optimization of regulatory resource allocation and enhances overall regulatory efficacy, moving beyond one-size-fits-all models.