  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.
