The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner’s objective is to progressively acquire new tasks as they arrive over time, while also guaranteeing statistical parity among various protected sub-populations, such as race and gender, when it comes to the newly introduced tasks. A significant limitation of current approaches lies in their heavy reliance on the i.i.d (independent and identically distributed) assumption concerning data, leading to a static regret analysis of the framework. Nevertheless, it’s crucial to note that achieving low static regret does not necessarily translate to strong performance in dynamic environments characterized by tasks sampled from diverse distributions. In this paper, to tackle the fairness-aware online learning challenge in evolving settings, we introduce a unique regret measure, FairSAR, by incorporating long-term fairness constraints into a strongly adapted loss regret framework. Moreover, to determine an optimal model parameter at each time step, we introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. This algorithm possesses the ability to adjust to dynamic environments by effectively managing bias control and model accuracy. The problem is framed as a bi-level convex-concave optimization, considering both the model’s primal and dual parameters, which pertain to its accuracy and fairness attributes, respectively. Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints. Our experimental evaluation on various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches rooted in the most advanced prior online learning methods.

具有公平意识的在线学习框架已成为持续终身学习背景下的有效工具。在这种情况下，学习者的目标是随着时间的推移逐步获得新任务，同时在涉及新引入的任务时保证各种受保护的子群体（例如种族和性别）之间的统计平等。当前方法的一个显着局限性在于它们严重依赖于有关数据的iid（独立同分布）假设，导致框架的静态遗憾分析。然而，重要的是要注意，实现低静态遗憾并不一定意味着在以从不同分布中采样的任务为特征的动态环境中表现出色。在本文中，为了应对不断变化的环境中公平意识在线学习的挑战，我们引入了一种独特的遗憾衡量标准 FairSAR，将长期公平约束纳入高度适应的损失遗憾框架中。此外，为了确定每个时间步长的最佳模型参数，我们引入了一种创新的自适应公平感知在线元学习算法，称为 FairSAOML。该算法具有通过有效管理偏差控制和模型精度来适应动态环境的能力。该问题被构建为双层凸凹优化，同时考虑模型的原始参数和对偶参数，这些参数分别与其准确性和公平性属性有关。理论分析得出了损失遗憾和公平约束累积违反的次线性上限。我们对动态环境中各种真实世界数据集的实验评估表明，我们提出的 FairSAOML ​​算法始终优于植根于最先进的现有在线学习方法的替代方法。