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.

通过引入长期公平性约束到适应性损失后悔框架中，我们在不断变化的环境中解决了公平感知在线学习挑战，并提出了一种独特的后悔度量 FairSAR。此外，为了确定每个时间步的最优模型参数，我们引入了一种创新的自适应公平感知在线元学习算法 FairSAOML，该算法通过有效管理偏差控制和模型准确性来适应动态环境。该问题被构建为双层凸凹优化，考虑了模型的准确性和公平性属性，理论分析得出了损失后悔和公平性约束累积违规的次线性上界。我们在动态环境中的各种真实数据集上的实验评估表明，我们提出的 FairSAOML 算法始终优于基于最先进在线学习方法的替代方法。