achine learning detects credit card fraud by recognizing unusual spending patterns, curates Netflix recommendations based on viewing history and, by identifying symptoms, can even inform a medical diagnosis. It’s seemingly everywhere, and next year—in a trailblazing new course integrating computer science and social studies—eligible juniors and seniors will learn machine learning techniques while examining its social and ethical implications.
“Artificial intelligence has the potential to act as a modern-day steam engine pushing the country in new directions,” says Dr. Michael Naragon, History Department Chair, of the advances driving what’s been dubbed the Fourth Industrial Revolution. “Deciding how we want to integrate technology into our daily lives requires deep thought and reflection. We want to ensure that our choices actually enhance people’s lives, promote greater equity, and further the public good.”
The Machine Learning course is a WT first, both because it was co-developed by Naragon and Computer Science Department Chair David Nassar, and because it will be co-taught. The idea grew organically as conversations between Naragon and Nassar revealed common themes covered in otherwise separate classrooms.

“Mike immediately came up with many possibilities: for example, recognizing patterns in someone’s speech or Tweets to determine political affiliation, or patterns in human trafficking to help stop the problem,” recalls Nassar. “The more we discussed it, the better it looked.”

Students will examine problems of interest to them by tackling sets of big data, generously made available through partnerships with CMU and other local experts. Big data is more than just a collection of lots of information. (Think of Google’s ability to predict one’s search before typing is complete, or all of the data necessary to teach an automobile to be autonomous.) According to Nassar, “We now not only have the ability to store all of this data, but we are also able to compute with it, and this is at the heart of many applications of machine learning. With our new ability to analyze this data we can better understand patterns of human behavior and choice.”

The course’s impact on students will be practical, philosophical, and far-reaching. But even as Machine Learning breaks new ground at WT, it does so while following an established path, asserts Naragon. “In the end, a WT education has always relied on mastery of foundational knowledge and disciplines not as the sole end but as a vehicle for deeper inquiry and understanding. This course—while unique in its organization and approach—fits quite well alongside the creative and deeply powerful courses taught by our colleagues.”