New York University

Technology, Inequality, and Public Policy
[undergraduate]
This course explores the societal implications of two kinds of technology: prediction models, which forecast people’s outcomes based on patterns in data, and generative artificial intelligence (AI), which produces content like text, images, and videos. By comparing these technologies to historical tools and systems, students will learn to analyze their potential to disrupt, reinforce, or amplify societal hierarchies, social divisions, and inequalities. The course will examine the social, ethical, and political dimensions of AI, with a focus on AI applications in public policy domains such as education, criminal justice, housing, and healthcare. It will also explore strategies for effectively governing these evolving technologies.

Generative AI in Society
[undergraduate, co-taught with BK Lee]
This course examines the opportunities and challenges posed by generative AI, which produces new content like text, images, audio, and computer code. It will offer a primer on how generative AI models like ChatGPT are constructed and how they work. Through hands-on activities and class discussions, students will further explore potential applications of these tools in social science research, as well as in other domains like medicine, law, and art. In addition, the course will offer students concepts and frameworks to critically analyze generative AI models and unpack the risks they present. By the end of the semester, students are expected to develop a well-rounded perspective on the rapidly evolving field of generative AI and its potential impact on society.

University of Notre Dame

Algorithms, Data, and Society
[undergraduate]
Description: Algorithms and data increasingly influence our behavior, steer resources, and inform institutional decisions that affect our everyday lives. This course will examine the social forces that shape what information gets recorded in databases and how algorithms are constructed and used. It will also introduce various approaches for assessing how algorithms and data impact the social world. Along the way, we’ll tackle important questions raised by these technological developments: What opportunities and challenges emerge when machine learning is applied to data about people? How should we evaluate whether algorithms are better or worse than the systems they replace? How might algorithms shape our agency, relationships, and access to opportunity?

Statistics for Sociological Research
[undergraduate]
Description: We frequently encounter statements or claims based on statistics, such as: “Women earn less than men,” “The American population is becoming more racially and ethnically diverse,” or “Married people are healthier than unmarried people.” On what information are these statements based? What kinds of evidence support or refute such claims? How can we assess their accuracy? This course will show students how to answer these sorts of questions by interpreting and critically evaluating statistics commonly used in the analysis of social science data. Hands-on data analysis and interpretation are an important part of the course. You will gain the skills to conduct quantitative data analysis using the R statistical programming language. You should finish the course with the ability to interpret, question, and discuss statistics accurately and with an understanding of which type of statistic is appropriate for different kinds of data and research questions. No prior statistical knowledge is required.