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。

具有公平意识的动态环境响应型在线元学习

Dynamic Environment Responsive Online Meta-Learning with Fairness  Awareness

The fairness-aware online learning framework has arisen as a powerful tool
for the continual lifelong learning setting. The goal for the learner is to
sequentially learn new tasks where they come one after another over time and
the learner ensures the statistic parity of the new coming task across
different protected sub-populations (e.g. race and gender). A major drawback of
existing methods is that they make heavy use of the i.i.d assumption for data
and hence provide static regret analysis for the framework. However, low static
regret cannot imply a good performance in changing environments where tasks are
sampled from heterogeneous distributions. To address the fairness-aware online
learning problem in changing environments, in this paper, we first construct a
novel regret metric FairSAR by adding long-term fairness constraints onto a
strongly adapted loss regret. Furthermore, to determine a good model parameter
at each round, we propose a novel adaptive fairness-aware online meta-learning
algorithm, namely FairSAOML, which is able to adapt to changing environments in
both bias control and model precision. The problem is formulated in the form of
a bi-level convex-concave optimization with respect to the model's primal and
dual parameters that are associated with the model's accuracy and fairness,
respectively. The theoretic analysis provides sub-linear upper bounds for both
loss regret and violation of cumulative fairness constraints. Our experimental
evaluation on different real-world datasets with settings of changing
environments suggests that the proposed FairSAOML significantly outperforms
alternatives based on the best prior online learning approaches.

本文提出了一种针对不断变化的环境的公平在线学习框架 FairSAOML，通过添加长期公平性约束来构建新的遗憾度量 FairSAR 和新的自适应公平在线元学习算法，提高分布不一样时的性能，同时对累积公平性约束和损失遗憾度提供了亚线性的理论上限，实验结果表明本文提出的算法在不同的数据集和环境中均优于当前最佳的在线学习算法。