In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

While machine learning can myopically reinforce social inequalities, it may also be used to
dynamically seek equitable outcomes. In this paper, we formalize long-term fairness as an …

Although many fairness criteria have been proposed for decision making, their long-term
impact on the well-being of a population remains unclear. In this work, we study the …

Recent studies in reinforcement learning (RL) have made significant progress by leveraging
function approximation to alleviate the sample complexity hurdle for better performance …

Reinforcement Learning from Human Feedback (RLHF) has been credited as the key
advance that has allowed Large Language Models (LLMs) to effectively follow instructions …

Abstract Realistically---and equitably---modeling the dynamics of group-level disparities in
machine learning remains an open problem. In particular, we desire models that do not …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Recent research has shown that seemingly fair machine learning models, when used to
inform decisions that have an impact on peoples' lives or well-being (eg, applications …

In this article, we introduce a novel learning automata (LA) solution to the nonlinear
stochastic proportional polling (NSPP) problem. The only available solution to this problem …