How Big Data Harms Poor Communities

Big data can help solve problems that are too big for one person to wrap their head around. It’s helped businesses cut costs, cities plan new developments, intelligence agencies discover connections between terrorists, health officials predict outbreaks, and police forces get ahead of crime. Decision-makers are increasingly told to “listen to the data,” and make choices informed by the outputs of complex algorithms. But when the data is about humans—especially those who lack a strong voice—those algorithms can become oppressive rather than liberating.

For many poor people in the U.S., the data that’s gathered about them at every turn can obstruct attempts to escape poverty. Low-income communities are among the most surveilled communities in America. Public-benefits programs, child-welfare systems, and monitoring programs for domestic-abuse offenders all gather large amounts of data on their users, who are disproportionately poor. In certain places, in order to qualify for public benefits like food stamps, applicants have to undergo fingerprinting and drug testing. Once people start receiving the benefits, officials regularly monitor them to see how they spend the money, and sometimes check in on them in their homes. Data gathered from those sources can end up feeding back into police systems, leading to a cycle of surveillance.