Bias and Discrimination in AI: A Cross-Disciplinary Perspective
Bias and Discrimination in AI: A Cross-Disciplinary Perspective
复制标题
DOI：
10.1109/mts.2021.3056293
复制
发表时间：
2021-06-01
影响因子：
2.2
通讯作者：
Criado, Natalia
中科院分区：
文献类型：
--
作者：
Ferrer, Xavier;van Nuenen, Tom;Criado, Natalia
自动化系统以大规模运行并影响大量人群，可以做出相应的、有时是有争议的决策。自动化决策可能会影响一系列现象，从信用评分到保险赔付再到健康评估。当这些形式的自动化使某些群体或个人处于系统劣势时，它们可能会出现问题。这些是歧视案件--在法律上被定义为基于某些受保护特征(也称为受保护属性)，如收入、教育、性别或种族，对个人(或群体)的不公平或不平等待遇。当不公平待遇是由自动决策造成的时，数字歧视的话题就出现了。自动决策通常由智能代理或其他基于人工智能的系统做出。数字歧视在许多领域都很普遍，例如警务风险评估系统和信用评分[1]、[2]。
Operating at a large scale and impacting large groups of people, automated systems can make consequential and sometimes contestable decisions. Automated decisions can impact a range of phenomena, from credit scores to insurance payouts to health evaluations. These forms of automation can become problematic when they place certain groups or people at a systematic disadvantage. These are cases of discrimination-which is legally defined as the unfair or unequal treatment of an individual (or group) based on certain protected characteristics (also known as protected attributes) such as income, education, gender, or ethnicity. When the unfair treatment is caused by automated decisions, usually taken by intelligent agents or other AI-based systems, the topic of digital discrimination arises. Digital discrimination is prevalent in a diverse range of fields, such as in risk assessment systems for policing and credit scores [1], [2].