Dynamic Environment Responsive Online Meta-Learning with Fairness
  Awareness
Chen Zhao Feng Mi Xintao Wu Kai Jiang Latifur Khan Feng Chen
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.