用机器学习提升招生公平性，效果比人类评委更稳定。
Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?
- 用多个模型和人类专家对比决策一致性，量化公平性。
- 模型在公平性上比人类高14.08%至18.79%，且准确率不下降。
- 适合关注算法公平性与人机协同的教育、招聘领域研究者。
机器学习（ML）与人类决策均存在偏见，如算法和数据驱动偏见，以及认知或主观偏见。本研究基于包含870名申请者档案的真实大学招生数据集，采用XGB、Bi-LSTM、KNN三种模型，并结合BERT嵌入处理文本特征。为评估个体公平性，提出一致性指标，衡量不同ML模型与具有多样背景的人类专家间决策的一致性。结果表明，ML模型在公平性一致性上优于人类评阅者，差异范围为14.08%至18.79%。研究显示，利用机器学习可在保持高准确率的同时增强招生公平性，支持人机协同的混合决策模式。
原文摘要 · Abstract (English)
Fairness in both Machine Learning (ML) predictions and human decision-making is essential, yet both are susceptible to different forms of bias, such as algorithmic and data-driven in ML, and cognitive or subjective in humans. In this study, we examine fairness using a real-world university admissions dataset comprising 870 applicant profiles, leveraging three ML models: XGB, Bi-LSTM, and KNN, alongside BERT embeddings for textual features. To evaluate individual fairness, we introduce a consistency metric that quantifies agreement in decisions among ML models and human experts with diverse backgrounds. Our analysis reveals that ML models surpass human evaluators in fairness consistency by margins ranging from 14.08\% to 18.79\%. Our findings highlight the potential of using ML to enhance fairness in admissions while maintaining high accuracy, advocating a hybrid approach combining human judgement and ML models.
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