Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection within a single learning process. Unlike traditional heuristic-driven filter or wrapper approaches, our RL agent adaptively selects features using a reward signal that explicitly integrates predictive performance with fairness considerations. This dynamic formulation allows the model to balance generalization, accuracy, and equity throughout the training process, rather than rely exclusively on pre-processing adjustments or post hoc correction mechanisms. In this paper, we describe the construction of a multi-component reward function, the specification of the agents action space over feature subsets, and the integration of this system with ensemble learning. We aim to provide a flexible and generalizable way to select features in environments where predictors are correlated and biases can inadvertently re-emerge.

翻译：静态特征排除策略往往无法防止隐藏依赖关系影响模型预测时产生的偏差。为解决这一问题，我们探索了一种强化学习框架，将偏差缓解与自动化特征选择整合到单一学习过程中。与传统的启发式过滤法或封装法不同，我们的强化学习智能体通过显式整合预测性能与公平性考量的奖励信号自适应地选择特征。这种动态建模使模型能够在整个训练过程中平衡泛化能力、准确性与公平性，而非完全依赖预处理调整或事后修正机制。本文阐述了多组分奖励函数的构建、智能体在特征子集上动作空间的规范，以及该系统与集成学习的融合。我们旨在为预测变量相关且偏差可能意外重现的环境提供一种灵活且可泛化的特征选择方法。