This paper examines the intricate relationship between differentiated and personalized trust-based regulation and the use of past behavior as a predictor of future compliance. As regulatory approaches evolve beyond traditional command-and-control methods toward compliance-based paradigms, the ability to differentiate between trustworthy and untrustworthy subjects becomes crucial. However, relying on past behavior for these predictions has presented fundamental challenges. This paper unpacks these challenges, including the variability in predictive power, context dependency, statistical interpretation issues, and ethical concerns. By analyzing these complexities in light of the promise of AI-powered predictive regulation, we aim to provide insights for policymakers and researchers in developing more effective and equitable trust-based regulatory technologies. This paper concludes with specific practical and ethical recommendations for the implementation of AI-based regulatory approaches based on behavior prediction.

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