论文标题
AI中的偏见和歧视：跨学科的观点
Bias and Discrimination in AI: a cross-disciplinary perspective
论文作者
论文摘要
随着对自动决策系统的人工智能（AI）的广泛和普遍使用，AI偏见变得越来越明显和有问题。它的负面后果之一是歧视：基于某些特征对个体的不公平或不平等的待遇。但是，偏见与歧视之间的关系并不总是很清楚。在本文中，我们从嵌入技术，法律，社会和伦理学方面的跨学科角度调查了有关AI中有关偏见和歧视的相关文献。我们表明，在人工智能中找到解决偏见和歧视的解决方案需要强大的跨学科合作。
With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.
