Data, global development, and COVID-19: Lessons and consequences
Wim Naudé, Ricardo Vinuesa
Abstract
The COVID-19 pandemic holds at least seven lessons for the relationship between data-driven decision making, the use of artificial intelligence, and development. These are that (1) in a global crisis, the shifting value of data creates policy pitfalls; (2) predictions of crises and how they play out are mostly wrong and can cause unhelpful panics; (3) digital deluges are as significant a problem as lack of data; (4) data deprivation and digital divides can deepen inequalities and hamper global coordination; (5) data creates regulatory dilemmas; (6) interoperability and reuse are critical aspects of data-driven decision making but are neglected; and (7) decentralization of data gathering and data use may reduce vulnerabilities to risk, and strengthen resilience of countries and regions. The consequences for data policy and data science are that trust, equality, context, and political leadership are as important, if not more, than technology.