Advancing Explainable and Secure Machine Learning for Decision Support in U.S. Regulated Systems

Abstract

Machine learning systems are increasingly deployed in U.S. regulated decision-support environments where predictive outputs must be accurate, interpretable, secure, privacy-preserving, and auditable. This study advanced an integrated quantitative assurance framework for evaluating explainable and secure machine learning in regulated contexts. Guided by a structured review of 118 peer-reviewed studies, a multi-phase experimental design was implemented incorporating predictive benchmarking, explanation fidelity and stability testing, adversarial robustness assessment, privacy inference evaluation, and human-in-the-loop experimentation. Baseline predictive accuracy across model families averaged 0.84 (SD = 0.04), with ensemble models reaching 0.86. Calibration error decreased from 0.041 in unconstrained models to 0.028 in constrained configurations. Under adversarial simulation, baseline models experienced a 14.2 percentage-point degradation in performance, whereas robustness-enhanced models limited degradation to 6.5 percentage points. Privacy controls reduced membership inference attack success rates from 0.64 (SD = 0.05) to 0.52 (SD = 0.04), with a modest 1.7% reduction in discrimination performance. Explanation fidelity reached 0.92 (SD = 0.02) for intrinsically interpretable models compared to 0.85 (SD = 0.04) for post hoc methods, and explanation stability variance decreased from 0.08 to 0.03 under enhanced configurations. In human-in-the-loop evaluation (N = 312; 6,240 trials), structured explanations increased decision accuracy from 0.72 (SD = 0.09) to 0.79 (SD = 0.07), improved confidence calibration from 0.74 to 0.82, and increased selective override behavior from 18.4% to 24.7%, while response time rose from 34.1 to 40.8 seconds. Regression models explained 34% of variance in decision accuracy and 38% in confidence calibration. Findings demonstrated that integrated evaluation of explain ability, robustness, and privacy produced measurable improvements in predictive validity, interpretability reliability, security resilience, and human decision performance within regulated systems.