Abstract
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 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 âsurveillance creepâ 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.
Highlights
In June 1955, the great Hungarian mathematician and polymath John Von Neumann published a popular essay entitled “Can we survive technology?” (Von Neumann, 1955)
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
Faced with the current public health crisis, data scientists and AI/ML innovators may be inclined to ask: Are we ready for this? Can we find a responsible path to wielding our technological efficacy ethically and safely? In what follows, I claim that this crossroads need not induce paralysis as to which way we should go, and, presents us with clear signage for finding the right way forward
Summary
In June 1955, the great Hungarian mathematician and polymath John Von Neumann published a popular essay entitled “Can we survive technology?” (Von Neumann, 1955). Von Neumann, stricken with terminal cancer, wrote about what he called “the maturing crisis of technology,” a situation in which the global effects of accelerating technological advancement were outpacing the development of ethical and political self-understandings that were capable of responsibly managing such an explosion of innovation This crisis, he feared, was creating unprecedented dangers of specieslevel self-destruction ranging from geoengineering and unbridled automation to nuclear holocaust. In less than a generation, exponential leaps in information-processing power have coalesced with the omnipresent data extraction capabilities of an ever more dynamic, integrated, and connected digital world to provide a fecund spawning ground for the explosion of AI/ML technologies As these innovations have advanced apace—as the scope of their impacts has come to stretch from the most intimate depths of self-development to the fate of the biosphere itself—we need ever more to reflect soberly on Von Neumann’s worry: Have we developed the novel ethical and political self-understandings, values, practices, and forms of life necessary to responsibly steer and constrain the rapid proliferation of AI/ML technologies?. Drawing upon current thinking in applied AI/ML ethics, social scientific approaches to data-driven technologies, and RRI, these steps suggest a means of attaining and assessing open, accountable, equitable, and democratically governed AI/ML processes and products
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