Fairness in classification tasks has traditionally focused on bias removal from neural representations, but recent trends favor algorithmic methods that embed fairness into the training process. These methods steer models towards fair performance, preventing potential elimination of valuable information that arises from representation manipulation. Reinforcement Learning (RL), with its capacity for learning through interaction and adjusting reward functions to encourage desired behaviors, emerges as a promising tool in this domain. In this paper, we explore the usage of RL to address bias in imbalanced classification by scaling the reward function to mitigate bias. We employ the contextual multi-armed bandit framework and adapt three popular RL algorithms to suit our objectives, demonstrating a novel approach to mitigating bias.

翻译：传统分类任务中的公平性研究主要聚焦于从神经表征中消除偏见，但近期趋势更倾向于将公平性嵌入训练过程的算法方法。这些方法引导模型实现公平性能，避免了因表征操作可能导致的宝贵信息损失。强化学习凭借其通过交互学习的能力，以及通过调整奖励函数来激励期望行为的特性，成为该领域极具前景的工具。本文探索利用强化学习解决不平衡分类中的偏见问题，通过调整奖励函数的尺度来缓解偏见。我们采用上下文多臂老虎机框架，并适配三种流行的强化学习算法以实现目标，展示了一种缓解偏见的新颖方法。