  The problem of learning to generalize to unseen classes during training,
known as few-shot classification, has attracted considerable attention.
Initialization based methods, such as the gradient-based model agnostic
meta-learning (MAML), tackle the few-shot learning problem by "learning to
fine-tune". The goal of these approaches is to learn proper model
initialization, so that the classifiers for new classes can be learned from a
few labeled examples with a small number of gradient update steps. Few shot
meta-learning is well-known with its fast-adapted capability and accuracy
generalization onto unseen tasks. Learning fairly with unbiased outcomes is
another significant hallmark of human intelligence, which is rarely touched in
few-shot meta-learning. In this work, we propose a Primal-Dual Fair
Meta-learning framework, namely PDFM, which learns to train fair machine
learning models using only a few examples based on data from related tasks. The
key idea is to learn a good initialization of a fair model's primal and dual
parameters so that it can adapt to a new fair learning task via a few gradient
update steps. Instead of manually tuning the dual parameters as hyperparameters
via a grid search, PDFM optimizes the initialization of the primal and dual
parameters jointly for fair meta-learning via a subgradient primal-dual
approach. We further instantiate examples of bias controlling using mean
difference and decision boundary covariance as fairness constraints to each
task for supervised regression and classification, respectively. We demonstrate
the versatility of our proposed approach by applying our approach to various
real-world datasets. Our experiments show substantial improvements over the
best prior work for this setting.
