Moss Emanuel, Metcalf Jacob
Data & Society Research Institute, New York, NY 10010, USA.
CUNY Graduate Center, New York, NY 10016, USA.
Patterns (N Y). 2020 Oct 9;1(7):100102. doi: 10.1016/j.patter.2020.100102.
The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.
在短短几个月的时间里，新冠疫情极大地重塑了全球社会。由于机器学习越来越被视为一种能够大规模解决重大问题的技术，机器学习应用被视为减轻大流行病风险的理想干预措施。然而，与许多技术官僚治理工具一样，机器学习与风险的社会产生和分配密切相关，在工程师和其他技术专家为当前危机开发工具时，必须考虑机器学习在风险产生中的作用。本文总体上描述了机器学习与风险的社会产生之间的耦合，特别是在应对疫情方面。接着，本文阐述了风险管理在科技行业将伦理制度化的努力中的作用，以及这些努力如何能够通过机器学习对风险的社会产生有更深入的理解而受益。
