Data-driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so-called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm-based decision-making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns.

中文翻译：

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算法偏差：感觉、来源、解决方案

数据驱动的算法被广泛用于在敏感领域做出或协助决策，包括医疗保健、社会服务、教育、招聘和刑事司法。在各种情况下，此类算法保留甚至加剧了对弱势社区的偏见，从而引发了一个充满活力的研究领域，专注于所谓的算法偏见。这项研究包括在基于算法的决策中识别、诊断和应对偏见的工作。本文旨在通过提供三个关键主题的概述来促进哲学分析在这些有争议的问题上的应用：什么是算法偏差？为什么以及如何发生？可以而且应该做些什么？在整个过程中，我们强调与哲学思想和关注点的实际和潜在的联系。