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

Area of Science:

• Sociotechnical studies
• Public health
• Computer science

Background:

• The COVID-19 pandemic necessitated rapid societal adaptation and technological interventions.
• Machine learning (ML) is perceived as a powerful tool for addressing large-scale challenges, including pandemic risks.
• However, ML's role in the social production and distribution of risk requires critical examination.

Purpose of the Study:

• To describe the intersection of machine learning and the social production of risk.
• To specifically analyze this coupling within the context of pandemic responses.
• To explore how understanding risk production through ML can inform ethical technology development and risk management.

Main Methods:

• Conceptual analysis of machine learning applications in crisis response.
• Examination of sociotechnical frameworks for understanding risk.
• Literature review on ML, governance, and ethical technology.

Main Results:

• Machine learning, while offering solutions, is inherently involved in how risks are socially constructed and allocated.
• This involvement is particularly evident in pandemic response scenarios.
• Current ethical initiatives in technology may benefit from a more nuanced understanding of ML-driven risk production.

Conclusions:

• A critical perspective on machine learning is necessary to address its role in risk.
• Ethical technology development requires acknowledging and analyzing the social dimensions of ML.
• Integrating insights into the social production of risk can enhance risk management and ethical governance in the tech industry.