Algorithmic fairness, studying how to make machine learning (ML) algorithms fair, is an established area of ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration when building ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e., the class label is given as a precondition. Unlike prior fairness work, we study individual fairness in learning with censorship where the assumption of availability of the class label does not hold, while still requiring that similar individuals are treated similarly. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment, and show how learning with such a relaxed precondition draws new insights that better explain algorithmic fairness. We also thoroughly evaluate the performance of the proposed methodology on three real-world datasets, and validate its superior performance in minimizing discrimination while maintaining predictive performance.

翻译：算法公平性，即研究如何使机器学习算法公平，是机器学习的一个成熟领域。随着机器学习技术扩展其应用领域，包括那些具有高社会影响的领域，在构建机器学习系统时考虑公平性变得至关重要。然而，尽管其在社会敏感应用方面范围广泛，大多数工作将算法偏见问题视为监督学习的内在属性，即类别标签作为前提条件给定。不同于以往的公平性工作，我们研究带审查学习中个体公平性，其中类别标签可用性的假设不成立，同时仍然要求相似个体受到相似对待。我们认为这一视角代表了现实世界应用部署中更现实的公平性研究模型，并展示了在这种宽松前提条件下学习如何衍生出更好解释算法公平性的新见解。我们还对三个真实世界数据集上提出的方法进行了全面评估，验证了其在最小化歧视的同时保持预测性能方面的优越表现。