From Accuracy to Reliability: A Trust-Centric Machine Learning Framework for Safe Decision-Making
: Accuracy alone is insufficient for machine learning systems that support high-stakes decisions under uncertainty and distribution shift. This paper proposes a trust-centric framework that integrates ensemble-based uncertainty estimation, maximum mean discrepancy (MMD) shift detection, a dependability signal, and safety-aware decision logic to prioritize reliable actions over raw predictive confidence. The framework first estimates prediction reliability and then modulates action selection through feasibility constraints and a conservative fall-back policy when dependability is low. Evaluation on three high-stakes decision scenarios under controlled distribution-shift stress tests shows that the proposed framework reduces high-confidence errors from 18.3% to 6.8%, decreases constraint violations from 12.7% to 1.4%, and improves decision stability from 0.42 to 0.22 relative to an accuracy-only baseline, while maintaining competitive average utility. Ablation results further show that uncertainty weighting, shift detection, and fallback control each contribute to these gains. These findings support the need for reliability-aware, constraint-aware decision systems in safety-critical applications.