This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks.

翻译：本文提出了一种新颖方法，用于在敏感信息仅部分已知的场景中增强算法公平性。具体而言，我们建议利用敏感属性身份不确定的样本来训练传统的机器学习分类器。该分类器最终预测中观察到的公平性提升表明，优先考虑模糊性（即非规范性）作为改善现实世界分类任务公平性保证的手段具有广阔前景。