Better manage risks inherent in Big Data

In the last 15 years, we have witnessed an explosion in the amount of digital data available - from the Internet, social media, scientific equipment, smart phones, surveillance cameras, and many other sources - and in the computer technologies used to process it. "Big Data", as it is known, will undoubtedly deliver important scientific, technological, and medical advances. But Big Data also poses serious risks if it is misused or abused.

But having more data is no substitute for having high-quality data. For example, a recent article in Nature reports that election pollsters in the United States are struggling to obtain representative samples of the population, because they are legally permitted to call only landline telephones, whereas Americans increasingly rely on cellphones. And while one can find countless political opinions on social media, these aren't reliably representative of voters, either. In fact, a substantial share of tweets and Facebook posts about politics are computer-generated.

A Big Data program that used this search result to evaluate hiring and promotion decisions might penalize black candidates who resembled the pictures in the results for "unprofessional hairstyles," thereby perpetuating traditional social biases. And this isn't just a hypothetical possibility. Last year, a ProPublica investigation of "recidivism risk models" demonstrated that a widely used methodology to determine sentences for convicted criminals systematically overestimates the likelihood that black defendants will commit crimes in the future, and underestimates the risk that white defendants will do so.