With Big Data Comes Big Responsibility

In recent years, social scientists, including UCLA’s Safiya Noble (see sidebar on page 36), have raised the alarm that if we rely on big data for social and economic purposes without heavily regulating its use, we risk reinforcing inequities.

UCLA economics professor and California Policy Lab faculty director Till von Wachter, for his part, is highly attuned to the sensitive nature of the data his group works with. Figuring out how to use highly personal data about, say, mental health treatment in a responsible, unbiased and farsighted way is a challenge that requires not just a commitment to justice but also expertise in law and data security. That complicated work is well worth it, however, with equitable research and policy as the goal.

“We’ve paid the fixed costs to create a legal framework, to have a highly secure IT infrastructure and to clean up the data,” von Wachter says of the California Policy Lab, which has been in the headlines lately for unique data-driven studies of California’s unemployment benefits system during the COVID-19 pandemic. “We also collaborate with our community advisory board for their insights on this work and we only work with anonymized data, all of which facilitates cooperation between agencies and researchers.”

When it comes to big data, discussions of the science involved can sometimes get abstruse. Its impact, however, ranges from the individual to a global scale—take climate science.

Alex Hall, professor in the department of atmospheric and oceanic sciences and the Institute of the Environment and

Sustainability as well as director of the Center for Climate Science at UCLA, observes that his field was one of the first to embrace big data several decades ago. Without algorithmically assisted analyses of vast troves of data, scientists never could have developed accurate next-day weather forecasts—let alone climate models that predict conditions decades or centuries from now.

What’s changed, Hall says, is the introduction of machine-learning analysis techniques. This data technology has made it possible to conduct new research, including his work on extreme precipitation events, one of the most catastrophic effects of climate change. Using artificial intelligence to detect changes in these phenomena, Hall’s team tested whether leading climate model predictions of increasing precipitation extremes were accurate. They were: Storms-wise, the real world is behaving according to climate change projections.

“We’re using machine learning to find pretty subtle signals that would otherwise be difficult to see,” Hall says. “We’re also experimenting with different ways to use AI to address the question of the distribution of wildfire risk and enable us to make skillful predictions.”

To tackle that and other research questions, Hall can count on legions of new trainees; in 2018, UCLA became the first U.S. college to offer a climate science major. After all, a grounding in climate science is synonymous with a strong education in handling big data.