Learning To Think Critically About Machine Learning - Liwaiwai

Students in the MIT course 6.036 (Introduction to Machine Learning) study the principles behind powerful models that help physicians diagnose disease or aid recruiters in screening job candidates. Now, thanks to the Social and Ethical Responsibilities of Computing (SERC) framework, these students will also stop to ponder the implicationsof these artificial intelligence tools, which sometimes come with their share of unintended consequences. Last winter, a team of SERC Scholars worked with instructor Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and the 6.036 teaching assistants to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning. The process was initiated in the fall of 2019 by Jacob Andreas, the X Consortium Assistant Professor in the Department of Electrical Engineering and Computer Science. SERC Scholars collaborate in multidisciplinary teams to help postdocs and faculty develop new course material. Because 6.036 is such a large course, more than 500 students who were enrolled in the 2021 spring term grappled with these ethical dimensions alongside their efforts to learn new computing techniques.