Tackling COVID-19 through responsible AI innovation: Five steps in the right direction
Harvard Data Science Review (2020)
58 Pages Posted: 23 Jul 2020
Date Written: May 14, 2020
Innovations in data science and artificial intelligence/machine learning (AI/ML) have a central role to play in supporting global efforts to combat COVID-19. The versatility of AI/ML technologies enables scientists and technologists to address an impressively broad range of biomedical, epidemiological, and socio-economic challenges. This wide-reaching scientific capacity, however, also raises a diverse array of ethical challenges. The need for researchers to act quickly and globally in tackling SARS-CoV-2 demands unprecedented practices of open research and responsible data sharing at a time when innovation ecosystems are hobbled by proprietary protectionism, inequality, and a lack of public trust. Moreover, societally impactful interventions like digital contact tracing are raising fears of “surveillance creep” and are challenging widely-held commitments to privacy, autonomy, and civil liberties. Pre-pandemic concerns that data-driven innovations may function to reinforce entrenched dynamics of societal inequity have likewise intensified given the disparate impact of the virus on vulnerable social groups and the life-and-death consequences of biased and discriminatory public health outcomes. To address these concerns, I offer five steps that need to be taken to encourage responsible research and innovation. These provide a practice-based path to responsible AI design and discovery centered on open, accountable, equitable, and democratically governed processes and products. When taken from the start, these steps will not only enhance the capacity of innovators to tackle COVID-19 responsibly, they will, more broadly, help to better equip the data science and AI/ML community to cope with future pandemics and to support a more humane, rational, and just society.
Keywords: COVID-19, AI ethics, responsible research and innovation, open science, digital contact tracing, public trust
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