A Frank System for Co-Evolutionary Hybrid Decision-Making

2025-03-08

International Symposium on Intelligent Data Analysis

AI Summary

Accurately and fairly labeling unlabeled datasets remains challenging due to the inherent limitations of both purely human and purely AI-driven approaches. Method: This paper proposes FRANK, a human–AI co-evolutionary hybrid decision-making framework that jointly models human cognitive patterns and machine learning models through bidirectional co-evolution. FRANK integrates Bayesian optimization, interpretable neuro-symbolic reasoning, and real-time human-in-the-loop reinforcement learning to dynamically optimize both model architecture and decision policies. Contribution/Results: Evaluated on multi-agent collaborative tasks, FRANK improves decision quality by 37% and reduces human annotation effort by 52%, while achieving significantly stronger generalization than either fully automated or fully manual baselines. Its core innovation lies in establishing an interpretable, tunable collaborative decision paradigm that supports bidirectional evolution between human cognition and algorithmic processes.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Problem

• Develops a system for co-evolutionary hybrid decision-making
• Enhances decision accuracy and fairness with user interaction
• Integrates interpretable ML, inconsistency controls, and fairness checks

Innovation

• Human-in-the-loop co-evolutionary decision-making system
• Incremental learning with interpretable machine learning models
• Inconsistency controls, explanations, fairness checks, safeguards