Representation learning is increasingly employed to generate representations that are predictive across multiple downstream tasks. The development of representation learning algorithms that provide strong fairness guarantees is thus important because it can prevent unfairness towards disadvantaged groups for all downstream prediction tasks. To prevent unfairness towards disadvantaged groups in all downstream tasks, it is crucial to provide representation learning algorithms that provide fairness guarantees. In this paper, we formally define the problem of learning representations that are fair with high confidence. We then introduce the Fair Representation learning with high-confidence Guarantees (FRG) framework, which provides high-confidence guarantees for limiting unfairness across all downstream models and tasks, with user-defined upper bounds. After proving that FRG ensures fairness for all downstream models and tasks with high probability, we present empirical evaluations that demonstrate FRG's effectiveness at upper bounding unfairness for multiple downstream models and tasks.

翻译：表示学习越来越多地被用于生成能够对多个下游任务进行预测的表示。因此，开发具有强公平性保证的表示学习算法具有重要意义，因为它可以防止所有下游预测任务中对弱势群体的不公平对待。为了防止所有下游任务中对弱势群体的不公平，关键在于提供具有公平性保证的表示学习算法。在本文中，我们正式定义了具有高置信度的公平表示学习问题。随后，我们引入了具有高置信度保证的公平表示学习（FRG）框架，该框架能为所有下游模型和任务提供限制不公平性的高置信度保证，且具有用户定义的上界。在证明FRG能以高概率确保所有下游模型和任务的公平性之后，我们通过实证评估展示了FRG在多个下游模型和任务中有效限制不公平性上界的能力。