A Framework for Optimizing Human-Machine Interaction in Classification Systems

Automated decision systems increasingly rely on human oversight to ensure accuracy in uncertain cases. This paper presents a practical framework for optimizing such human-in-the-loop classification systems using a double-threshold policy. Instead of relying on a single decision cutoff, the system defines two thresholds (a lower and an upper) to automatically accept or reject confident cases while routing ambiguous ones for human review. We formalize this problem as an optimization task that balances system accuracy against human review workload and demonstrate its behavior through extensive Monte Carlo simulations. Our results quantify how different probability score distributions affect the efficiency of human intervention and identify the regions of diminishing returns where additional review yields minimal benefit. The framework provides a general, reproducible method for improving reliability in any decision pipeline requiring selective human validation, including applications in entity resolution, fraud detection, medical triage, and content moderation.