Teaching

I want my students to understand how data and AI systems work and to judge whether they should be used. Technical skill on its own isn't enough. Neither is ethical vocabulary.

Students don’t learn judgment by hearing about it. They learn it by making a decision and defending it with evidence. So I ask students to take positions on real systems, and I grade how well their reasoning holds up. Which side they take doesn’t matter to me. I also think people judge technology better once they have built some of it. Building shows students that every system is a series of choices someone made, and that those choices could have gone differently. For that reason I don’t teach technical skills first and ethics later. Students work on both in the same assignment.

My research and teaching are closely linked. I study how people inside tech organizations decide what counts as an ethical problem, and in class I ask students to make those calls themselves. I also draw on my years of fieldwork in Pakistan. Students see that fairness and accountability look different under different laws and institutions. Outside class, I mentor undergraduates doing research on how non-experts audit AI systems.

Lecturer, Data and Algorithms: Ethics and Policy (LIS 461)

The Information School, University of Wisconsin-Madison, Spring 2026
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I teach both the 4-credit (Comm-B) and 3-credit versions of LIS 461, an undergraduate course that examines the ethical, social, and policy implications of data-driven systems. The course introduces students to key debates around algorithmic bias, surveillance, predictive analytics, and the politics of data, with a strong emphasis on critical reflection and participatory ethics.

• Designed and currently lead all aspects of the course, including curriculum design, weekly lectures, assessments, and in-class activities.

• Supervise a teaching assistant and coordinate with three graders to support over 130 students, manage grading processes, and facilitate structured peer review activities.

• Developed content that supports student engagement with topics such as data justice, sociotechnical systems, algorithmic accountability, and the impact of AI systems on marginalized communities.

Lecturer, Introduction to Digital Information (LIS 351)
The Information School, University of Wisconsin-Madison, Summer 2026
View Syllabus (PDF)

LIS 351 is an undergraduate course on the technologies and policy issues behind digital information, from how the web and databases organize information to what that means for privacy, accessibility, and AI in organizations. I taught it as a fully online, asynchronous, eight-week summer course.

• Taught the course for asynchronous online delivery, including recorded weekly lectures, 11 quizzes, and 8 hands-on assignments.

• Led two technical projects: students hand-coded a website in HTML and CSS without web-authoring tools, and designed a relational database with an ER diagram, writing SQL queries to answer business questions.

• Taught usability and accessibility testing, personal privacy risk assessment, and a unit on how AI is reshaping organizations and students' own fields.