Tackling COVID-19 through Responsible AI Innovation: Five Steps in the\n Right Direction

Abstract

Innovations in data science and AI/ML have a central role to play in\nsupporting global efforts to combat COVID-19. The versatility of AI/ML\ntechnologies enables scientists and technologists to address an impressively\nbroad range of biomedical, epidemiological, and socioeconomic challenges. This\nwide-reaching scientific capacity, however, also raises a diverse array of\nethical challenges. The need for researchers to act quickly and globally in\ntackling SARS-CoV-2 demands unprecedented practices of open research and\nresponsible data sharing at a time when innovation ecosystems are hobbled by\nproprietary protectionism, inequality, and a lack of public trust. Moreover,\nsocietally impactful interventions like digital contact tracing are raising\nfears of surveillance creep and are challenging widely held commitments to\nprivacy, autonomy, and civil liberties. Prepandemic concerns that data-driven\ninnovations may function to reinforce entrenched dynamics of societal inequity\nhave likewise intensified given the disparate impact of the virus on vulnerable\nsocial groups and the life-and-death consequences of biased and discriminatory\npublic health outcomes. To address these concerns, I offer five steps that need\nto be taken to encourage responsible research and innovation. These provide a\npractice-based path to responsible AI/ML design and discovery centered on open,\naccountable, equitable, and democratically governed processes and products.\nWhen taken from the start, these steps will not only enhance the capacity of\ninnovators to tackle COVID-19 responsibly, they will, more broadly, help to\nbetter equip the data science and AI/ML community to cope with future pandemics\nand to support a more humane, rational, and just society.\n

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