Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains
arXiv:2603.21610v2 Announce Type: replace Abstract: Compliance monitoring in rule-governed domains (tax administration, clinical protocol adherence, environmental regulation) faces three structural obstacles that standard machine learning does not simultaneously address: the absence of labeled outcomes at deployment, strategically missing observations where non-compliant entities selectively withhold evidence, and a regulatory environment that changes faster than any supervised model can be retrained. We introduce Rule-State Inference (RSI), a Bayesian framework that reverses the usual paradig
The increasing complexity of regulatory environments and the limitations of traditional machine learning methods in contested domains necessitate new approaches to compliance monitoring.
This Bayesian framework addresses critical challenges in compliance, including missing data and rapidly evolving regulations, which impacts various rule-governed sectors.
By reversing the usual paradigm, RSI offers a more robust and adaptive method for detecting non-compliance in environments where outcomes are unlabeled and actors are strategic.
- · Regulatory bodies
- · Compliance software providers
- · Financial institutions
- · Environmental agencies
- · Entities engaged in strategic non-compliance
- · Legacy compliance systems
- · Fraudulent actors
Improved detection of non-compliant behavior in complex regulatory landscapes.
Increased operational efficiency and fairness in enforcement through more accurate and adaptive monitoring.
Potentially, a reduced scope for regulatory arbitrage and illicit activities across various sectors, leading to a more transparent and just operational environment.
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Read at arXiv cs.LG