High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response

• Main Authors: Emanuel Moss, Jacob Metcalf
• Format: Article
• Language: English
• Published: Elsevier 2020-10-01
• Series: Patterns
• Subjects: DSML 1: Concept: Basic principles of a new data science output observed and reported
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666389920301367

Summary: 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. The Bigger Picture: This paper describes the coupling of machine learning and the social production of risk in general, with specific illustrations drawn from machine learning applications in response to the COVID-19 pandemic. As the COVID-19 pandemic has drastically reshaped society around the world, many have looked to machine learning as a technology capable of addressing large problems at scale, and 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. Therefore, the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. The paper concludes by describing the role of risk management in the effort to institutionalize ethics in the technology industry, and how such efforts can benefit from understanding the social production of risk through machine learning.