Fairness Regret

Fairness regret addresses the challenge of balancing reward maximization with equitable treatment of different options in sequential decision-making problems, particularly within multi-armed bandit frameworks. Current research focuses on developing algorithms, such as those based on cooperative learning and federated learning, that achieve both low reward regret (suboptimal reward) and low fairness regret (unequal distribution of selections). These methods are being applied to various settings, including online advertising, resource allocation in IoT systems, and reinforcement learning, with a strong emphasis on theoretical guarantees of sublinear regret. The development of fair and efficient algorithms has significant implications for mitigating bias and ensuring equitable outcomes in diverse applications of machine learning.