Towards Fair In-Context Learning with Tabular Foundation Models

• URL: http://arxiv.org/abs/2505.09503v3
• Date: Fri, 01 Aug 2025 15:57:48 GMT
• Title: Towards Fair In-Context Learning with Tabular Foundation Models
• Authors: Patrik Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji,
• Abstract summary: We present the first investigation of fairness in Transformer-based in-context learning (ICL)<n>We evaluate three recently proposed foundation models -- TabPFNv2, TabICL, and TabDPT -- on benchmark datasets.<n>Our experiments show that the uncertainty-based strategy consistently improves group fairness metrics with minimal impact on predictive accuracy.
• Score: 6.4989916051093815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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