In this paper, we advocate for the study of fairness techniques in low data situations. We propose two algorithms Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on similar but slightly different tasks within a given domain. The second is a fair meta-learning approach to train models that can be trained through gradient descent with the objective of "learning how to learn fairly". This method encodes more general notions of fairness and accuracy into the model so that it can learn new tasks within a domain both quickly and fairly from only a few training points. We demonstrate experimentally the individual utility of each model using relevant baselines for comparison and provide the first experiment to our knowledge of K-shot fairness, i.e. training a fair model on a new task with only K data points. Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.

通过提出两种算法：公平警示和公平元学习算法，针对分享和传递公平的机器学习工具引发的关注，提出了一个在不同领域中训练一个公平的模型的可解释边界条件和同时平衡公平性和准确性在少量实例中快速微调的公平元学习方法。我们展示了每个模型的实用性，并证明了这两个算法的组合方法在训练新任务的模型时具有解释性的边界条件问题。

公正提示和公正-MAML：在最小数据情况下公正学习