  Innovations in data science and 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 socioeconomic 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. Prepandemic 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/ML 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.
