Beyond Accuracy: Measuring Logical Compliance of Predictive Models

Researchers introduce the Rule Violation Score (RVS), a new evaluation metric that measures whether predictive models respect logical and domain-specific constraints independently of accuracy. Unlike traditional metrics focused on prediction performance, RVS distinguishes between hard rules (strict constraints) and soft rules (statistical regularities), enabling assessment of logical consistency in high-stakes applications like finance and healthcare.

The introduction of the Rule Violation Score addresses a critical gap in machine learning evaluation methodology. Traditional metrics like accuracy and prediction error focus solely on whether models match ground truth, ignoring whether outputs violate predefined logical constraints—a significant blind spot in domains where regulatory compliance, safety, and interpretability matter as much as raw performance.

This research emerges from growing recognition that predictive accuracy alone provides an incomplete picture of model reliability. In financial services, autonomous systems, and healthcare, models must operate within logical boundaries defined by domain expertise, regulations, or safety requirements. A model achieving 95% accuracy while systematically violating critical business rules poses unacceptable risks. The RVS framework allows developers to quantify this trade-off explicitly.

The practical value extends beyond model evaluation. By treating hard and soft rules differently and enabling automatic SQL query generation for Horn rules, RVS offers actionable insights into which specific constraints models struggle with. The benchmarking across knowledge graphs and relational data demonstrates that models with identical accuracy metrics can exhibit dramatically different logical compliance profiles—a finding that should prompt industry reconsideration of evaluation standards.

For the AI industry, this work signals movement toward more rigorous governance frameworks. As machine learning systems deploy in regulated sectors, metrics capturing both performance and constraint adherence become essential infrastructure. The emphasis on dataset consistency evaluation also addresses upstream data quality issues that traditional metrics overlook. Future adoption could reshape how organizations validate models before production deployment.

• →RVS measures logical compliance independently of predictive accuracy, revealing behavioral differences hidden by standard metrics.
• →Models with identical accuracy can violate logical constraints at substantially different rates.
• →The metric distinguishes between hard rules (strict constraints) and soft rules (statistical regularities).
• →RVS can evaluate dataset logical consistency and identify poorly defined rules during development.
• →Automatic SQL generation for Horn rules makes the framework practical for real-world relational data systems.