A Framework for Optimizing Human-Machine Interaction in Classification Systems

2026-01-09

arXiv.org

AI Summary

This work addresses the trade-off between human annotation cost and system accuracy in human-in-the-loop classification by proposing an optimization framework based on a dual-threshold strategy. By setting upper and lower confidence thresholds, the system automatically processes high-certainty samples while routing only ambiguous cases to human reviewers. The approach formalizes the human-AI collaboration problem, identifies the critical region where human intervention yields diminishing returns, and quantifies the marginal benefit of manual review across diverse scenarios through probabilistic score modeling, Monte Carlo simulation, and optimization algorithms. Empirical evaluations demonstrate the framework’s generality and effectiveness across multiple domains—including entity resolution, fraud detection, medical triage, and content moderation—achieving high accuracy while substantially reducing human workload.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Human-in-the-loop
• Classification systems
• Decision thresholds
• Human review workload
• Accuracy optimization

Innovation

• human-in-the-loop
• double-threshold policy
• classification optimization
• Monte Carlo simulation
• decision system reliability