Training Fair Tabular Foundation Models

• URL: http://arxiv.org/abs/2608.14211v1
• Date: Fri, 14 Aug 2026 11:40:04 GMT
• Title: Training Fair Tabular Foundation Models
• Abstract summary: We propose a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer.<n>Experiments on 132 fairness tasks show consistent improvements in fairness while maintaining competitive accuracy.
• Score: 16.092437823158402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training. Despite the increased use of TFMs in high-stakes decision-making, their fairness properties remain largely unexplored. In this work, we incorporate fairness constraints directly into TFM training, enabling fair predictions in a single forward pass. Our approach addresses two key challenges: limited access to sensitive attributes in training data, and the incompatibility of existing fairness techniques with the in-context learning paradigm. We propose FairTFM, a scalable training strategy based on synthetic fairness tasks and a fairness-aware architecture using a gradient reversal layer, which encourages the model to learn representations invariant to sensitive attributes. Experiments on 132 fairness tasks show consistent improvements in fairness while maintaining competitive accuracy.

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