论文标题
保守的人工智能和社会不平等：通过社会理论概念化偏见的替代方案
Conservative AI and social inequality: Conceptualizing alternatives to bias through social theory
论文作者
论文摘要
为了回应呼吁从社会科学和人文学科在人工智能系统的发展，治理和研究中更大的跨学科参与时，本文介绍了一位社会学家对算法偏见和社会偏见再生产问题的看法。 AI中偏见的讨论涵盖了与研究不平等的社会学家相同的概念领域，长期以来使用更具体的术语和理论理解。对重现社会偏见的担忧应通过了解不平等在社会中不平等的方式来告知 - AI系统是同谋或可以设计以破坏和反击的过程。这里提出的对比是对AI的保守和激进方法之间的，保守主义指的是繁殖和加强现状的主导趋势，而激进的方法则可以破坏不平等的系统性形式。对阶级，性别和种族偏见的保守方法的局限性作为具体例子，以及这些领域中偏见与之相关的社会结构和过程。鉴于这些系统对人类生活的影响，社会问题不再是AI和机器学习的范围。这需要与越来越多的关键AI奖学金的体系进行交战，该奖学金超越了偏见的数据，以分析结构化的不平等现象，从而为根本性替代方案提供了可能性。
In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist's view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of the ways that inequality is continually reproduced in society -- processes that AI systems are either complicit in, or can be designed to disrupt and counter. The contrast presented here is between conservative and radical approaches to AI, with conservatism referring to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. The limitations of conservative approaches to class, gender, and racial bias are discussed as specific examples, along with the social structures and processes that biases in these areas are linked to. Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality, opening up the possibility for radical alternatives.
