This seminar will explore in depth the many ways in which modern computing systems — including the data they ingest, the decisions made by the folks who develop them, and their myriad and nearly ubiquitous applications — may enable, encourage, or prevent societal discrimination and surveillance capitalism of various types. Students will learn how algorithms and artificial intelligence (“AI”) systems work, how such algorithms and systems may provide differential treatment and/or outcomes for different populations, and how they may invade privacy and cause other harms to people. Students will also consider the potential legal/regulatory, technical, and social/policy interventions that could ameliorate the harms caused by such algorithms and systems and will weigh the advantages and disadvantaged of each.
At the end of the seminar, students should be able to (i) discuss with colleagues and others the positive and negative consequences of various current AI innovations, and (ii) suggest different approaches to address systemic bias and surveillance capitalism — including legal/regulatory or social/policy changes, as well as technical solutions — and explain why certain of these approaches might or might not work in specific circumstances.
Method of Evaluation: There will be three components to a student's grade, each worth 30% to 40% of the total course grade. The first component will consist of a reflective journal, which includes an entry for each class session and a summary for the whole semester. The second component will consist of the quality of class participation. The third component will consist of a small-group final project and presentation that could involve (depending on group preference) creating a website, podcast, video, or conducting interviews or research related to the topics covered in the course, among many other options. Each component will comprise 30% of a student’s grade. The remaining 10% will be allocated to one of the three components, depending on each student's strongest performance. In other words, the component in which the students receives the highest grade will be counted for 40%, rather than 30% of the student’s grade.