ABSTRACT

The mounting evidence of unintended harmful social consequences of automated algorithmic decision-making (AADM), powered by AI and big data, in transformative services (e.g., welfare services), is startling. The algorithmic harm experienced by individuals, communities and society-at-large involves new injustice claims and disputes that go beyond issues of social justice. Drawing from the theory of “abnormal justice” in this paper we articulate a new theory of algorithmic justice that addresses the questions: WHAT is the matter of algorithmic justice? WHO counts as a subject of algorithmic justice? HOW are algorithmic justices performed? and How to address and resolve disputes about the WHAT, WHO and HOW of algorithmic justice? We illustrate the theory of algorithmic justice by drawing from a case of AADM in social welfare services, widely adopted by governments around the world. Our research points to datafication, technological inscribing and the systemic nature of injustices as important IS-specific aspects of algorithmic justice. Our main practical contribution comes from the articulation of algorithmic justice as a framework that (1) makes visible the injustices related to the “what”, “who”, and “how” of AADM in transformative services, and (2) provides further insights into how we might address and resolve these algorithmic injustices.

摘要

越来越多的证据表明，在变革性服务（例如福利服务）中，由人工智能和大数据驱动的自动算法决策 (AADM) 会产生意想不到的有害社会后果，这一点令人吃惊。个人、社区和整个社会所经历的算法伤害涉及超出社会正义问题的新的不公正主张和争议。从本文中的“异常正义”理论出发，我们阐述了一种新的算法正义理论，它解决了以下问题：算法正义的问题是什么？谁算作算法正义的主体？如何执行算法正义？如何解决和解决关于算法正义的内容、谁和如何的争议？我们通过社会福利服务中的 AADM 案例来说明算法正义理论，被世界各国政府广泛采用。我们的研究指出数据化、技术铭刻和不公正的系统性是算法公正的重要的特定于 IS 的方面。我们的主要实际贡献来自于将算法正义作为一个框架进行阐述，该框架 (1) 使与 AADM 在变革性服务中的“什么”、“谁”和“如何”相关的不公正可见，并且 (2) 提供了进一步的见解探讨我们如何解决和解决这些算法上的不公正问题。