首次研究表格数据上下文学习的公平性，提出提升公平性的新方法。
Towards Fair In-Context Learning with Tabular Foundation Models
- 用不确定度筛选上下文样本，提升公平性
- 该方法在多个数据集上显著改善群体公平指标
- 适合关注机器学习公平性的研究人员使用
基于Transformer的表格基础模型最近在结构化数据的上下文学习（ICL）任务中表现出色，成为梯度提升树的有力替代方案。然而，这一新范式带来的公平性问题尚未被充分探讨。本文首次系统评估了三种前沿基础模型（TabPFNv2、TabICL、TabDPT）在多个基准数据集上的公平性表现。为缓解偏差，探索了三种预处理公平性增强方法：特征与敏感属性去相关、组平衡采样、基于不确定性的样本选择。实验表明，基于不确定性的策略在几乎不损失预测准确率的前提下，持续提升了群体公平性指标（如人口均等性、平等机会和等效几率）。代码已开源以促进可复现性。
原文摘要 · Abstract (English)
Transformer-based tabular foundation models have recently demonstrated promising in-context learning (ICL) performance on structured data, emerging as competitive alternatives to gradient-boosted trees. However, the fairness implications of this new paradigm remain largely unexplored. We present the first investigation of fairness in tabular ICL, evaluating three recently proposed foundation models--TabPFNv2, TabICL, and TabDPT--on multiple benchmark datasets. To mitigate biases, we explore three pre-processing fairness-enhancing methods: correlation removal (decorrelating input features from the sensitive attribute), group-balanced sample selection (ensuring equal representation of protected groups in context examples), and uncertainty-based sample selection (prioritizing context examples with high sensitive-attribute prediction uncertainty). Our experiments show that the uncertainty-based strategy consistently improves group fairness metrics (e.g., demographic parity, equalized odds, and equal opportunity) with minimal impact on predictive accuracy. We release our code to facilitate reproducibility https://github.com/patrikken/Fair-TabICL.
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