aiqbench analysis
This research introduces FairTFM, a scalable training strategy that integrates fairness constraints directly into Tabular Foundation Models to ensure equitable predictions without sacrificing accuracy. By addressing the incompatibility of existing techniques with in-context learning paradigms and handling limited access to sensitive attributes, this work advances critical safety considerations for deploying AI in high-stakes decision-making environments.
Training Fair Tabular Foundation Models
arXiv:2608.14211v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.14211 [cs.LG]
(or arXiv:2608.14211v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.14211 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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