A Framework for Optimizing Human-Machine Interaction in Classification Systems
For practitioners deploying classification systems with human oversight, this provides a general method to reduce human workload while maintaining accuracy, though the results are derived from simulations rather than real-world validation.
This paper introduces a double-threshold policy for human-in-the-loop classification systems that routes ambiguous cases to human reviewers while automatically accepting or rejecting high-confidence ones. The framework optimizes the trade-off between accuracy and human review cost, demonstrating through simulations that it can improve efficiency and identify diminishing returns.
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. Conventional classifiers usually produce a confidence score and apply a single cutoff, but our approach uses two thresholds (a lower and an upper) to automatically accept or reject high-confidence cases while routing ambiguous instances to human reviewers. We formulate this problem as an optimization task that balances system accuracy against the cost of human review. Through analytical derivations and Monte Carlo simulations, we show how different confidence score distributions impact the efficiency of human intervention and reveal 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.