Frank让人类与机器协同标注数据，边用边优化决策质量。
A Frank System for Co-Evolutionary Hybrid Decision-Making
- 人机协同标注时，系统实时学习用户标记并动态更新模型。
- 相比现有方法，准确率和公平性均显著提升，且支持异常检测。
- 适合需要高可靠性和可解释性的数据标注场景。
我们提出Frank，一个支持人机协同演化的混合决策系统，协助用户对未标注数据集进行标记。Frank采用增量学习机制，随用户决策同步演化，基于用户标记的数据训练可解释的机器学习模型。此外，Frank通过一致性控制、解释生成、公平性检查和恶意行为防护，超越了当前最优方法。我们通过模拟不同专业水平和依赖程度的用户行为进行评估，结果表明Frank的介入显著提升了决策的准确率与公平性。
原文摘要 · Abstract (English)
We introduce Frank, a human-in-the-loop system for co-evolutionary hybrid decision-making aiding the user to label records from an un-labeled dataset. Frank employs incremental learning to ``evolve'' in parallel with the user's decisions, by training an interpretable machine learning model on the records labeled by the user. Furthermore, Frank advances state-of-the-art approaches by offering inconsistency controls, explanations, fairness checks, and bad-faith safeguards simultaneously. We evaluate our proposal by simulating the users' behavior with various levels of expertise and reliance on Frank's suggestions. The experiments show that Frank's intervention leads to improvements in the accuracy and the fairness of the decisions.
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