Towards Fair In-Context Learning with Tabular Foundation Models
Published in Transactions on Machine Learning Research | 1st ICML Workshop on Foundation Models for Structured Data, 2026
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.
@article{
kenfack2026towards,
title={Towards Fair In-Context Learning with Tabular Foundation Models},
author={Patrik Kenfack and Samira Ebrahimi Kahou and Ulrich A{\"\i}vodji},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2026},
url={https://openreview.net/forum?id=AsBhwD0sqo},
note={}
}