Making models algorithmically fairer in tabular data has been long studied, with techniques typically oriented towards fixes which usually take a neural model with an undesirable outcome and make changes to how the data are ingested, what the model weights are, or how outputs are processed. We employ an emergent and different strategy where we consider updating the model's architecture and training hyperparameters to find an entirely new model with better outcomes from the beginning of the debiasing procedure. In this work, we propose using multi-objective Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) in the first application to the very challenging domain of tabular data. We conduct extensive exploration of architectural and hyperparameter spaces (MLP, ResNet, and FT-Transformer) across diverse datasets, demonstrating the dependence of accuracy and fairness metrics of model predictions on hyperparameter combinations. We show that models optimized solely for accuracy with NAS often fail to inherently address fairness concerns. We propose a novel approach that jointly optimizes architectural and training hyperparameters in a multi-objective constraint of both accuracy and fairness. We produce architectures that consistently Pareto dominate state-of-the-art bias mitigation methods either in fairness, accuracy or both, all of this while being Pareto-optimal over hyperparameters achieved through single-objective (accuracy) optimization runs. This research underscores the promise of automating fairness and accuracy optimization in deep learning models.

翻译：表格数据中模型的算法公平性优化已得到长期研究，传统方法通常针对有不良结果的神经网络模型进行修复，通过调整数据输入方式、模型权重或输出处理来改进。我们采用一种新兴且不同的策略，通过更新模型架构和训练超参数，在去偏过程初期即寻找具有更优结果的完全新模型。本研究首次将多目标神经架构搜索（NAS）与超参数优化（HPO）应用于极具挑战性的表格数据领域。我们系统探索了多种数据集上的架构与超参数空间（包括MLP、ResNet和FT-Transformer），揭示了模型预测的准确性和公平性指标对超参数组合的依赖性。研究表明，仅以准确率为目标通过NAS优化的模型往往无法从根本上解决公平性问题。我们提出了一种新方法，在准确性和公平性的多目标约束下联合优化架构与训练超参数。该方法生成的架构在公平性、准确性或两者兼有的维度上持续帕累托主导现有最先进的偏差缓解方法，同时这些架构在单目标（准确性）优化中获得的超参数组合上仍保持帕累托最优性。本研究凸显了深度学习模型中自动化公平性与准确性联合优化的巨大潜力。