By Michael Pearson, Executive Director of the Mathematical Association of America

The topic of ethics was very much on my mind when I attended a meeting of the National Academy’s Roundtable on Data Science Postsecondary Education, the theme of which was Motivating Data Science Education through Social Good.

There were a variety of presentations on specific programs that engage students in using data to help solve problems that matter to local communities, often highlighting less-glamorous-sounding applications that turn out to provide rich experiences for students to work on problems that they care about (or learn to care about through their experience).

Throughout the meeting, however, I was reminded of the thorny issues that we face as a society as a consequence of the enormous data streams that are being collected on all of us, and most especially those of us who routinely use (so-called) smart phones. What, for example, does privacy mean when our location is being tracked, and data on our movement is being sold in the marketplace? When records of our purchases are used to market additional products and services based on that history?

One of the presenters at the meeting was DJ Patil, mentioned above, who recently co-authored a (freely-available) ebook, Ethics and Data Science. In his presentation, DJ talked primarily about opportunities to use data science to support “good” efforts, such as search and rescue in the wake of natural disasters.

The discussions provided in Ethics and Data Science go further than examples of “data science for good” and argue that it is essential to do “good data science” that carefully considers not just the short-term goals for the business/entity that sponsors a particular project, but the larger implications for the populations from which the data is drawn.

It’s no longer news that law enforcement agencies mine social media accounts to gather intelligence on those it suspects have engaged (or might engage) in illegal behavior. The unprecedented ability to use data that can be gathered without warrants or probable cause raises serious issues about what privacy means in today’s environment. As Andrew Ferguson makes clear in his 2017 book, The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement, the use of such techniques is only growing. As he notes in the book,

While these predictive technologies are excitingly new, the concerns underlying them remain frustratingly old-fashioned. Fears of racial bias, a lack of transparency, data error and the distortions of constitutional protections offer serious challenges to the development of workable person-based predictive strategies. Yet person-based policing systems are being used now, and people are being targeted.

All of this reinforces my belief that mathematicians need to engage not only in data science, but with fundamental questions about the role of mathematics across the social, business, and political spectrum. While the answers are unlikely to be obvious, and consensus or even compromise around such issues even harder to reach, it is our responsibility to help inform our fellow citizens, and policy makers, on these complex issues.