Socio-Political Analysis and AI Courses
Academic Year 2024-2025
Paige Bollen. "Is Anything Data Now? Leveraging AI to Measure and Analyze New Sources of Data."
This proposed course centers on learning how to use AI to construct and assess new measures of critical yet often elusive social science concepts. It will have three interrelated goals. The first is to help students identify and access new sources of data. With the ever-increasing number of potential data sources, a core component of this course will be providing students with an introduction to types of data, where to access them, and how to understand their terms of use. The second goal of the course is to teach students how to construct measures from these data. Working through each data type, students will be introduced to models and methods commonly used to build measures from these data and the role of machine learning and generative AI in facilitating these analyses. The final goal of the course is to discuss the promises and perils of AI and machine learning in the analysis of social science data. Moving beyond simply applying these tools, students will also learn how to evaluate these models and their outputs and, as a result, think critically about the validity of their analyses and measures. Students will put these theories and skills into practice through a major data project they will work on throughout the semester, starting with identifying novel data related to their substantive interest and ending with a proposed measure or analysis that leverages AI.
This course will be available for graduate-level students and advanced undergraduates.
Ryan Kennedy. "Generative Artificial Intelligence and Political Analysis.”
This course will introduce students to the use of generative AI in political analysis. The advent of advanced generative AI, in the form of LLMs and their extensions, has potentially far-reaching repercussions for how research is conducted in the social sciences. But leveraging these tools requires a very different skillset and knowledge base than conducting traditional analyses. Undergraduate students who complete this course will gain an understanding of the methods for using generative AI in their research. Students will also learn about the potential limits and abuses of AI in political applications. Among the topics covered will be structure and function of transformers; applications of generative AI in political and governmental tasks; limitations of generative AI; and ethics of generative AI. Among the skills students will learn are prompt engineering and plain language programming; interaction with online APIs; some programming in R, Python and JavaScript; and A/B testing. The course will also reinforce students’ statistics and research design knowledge from previous courses.
This course is designed as an upper-level undergraduate political methods course.
Ju Yeon (Julia) Park. "Traditions and Transitions in Research Methods."
This course is designed to introduce undergraduate students in the university’s new CSS program to research methodologies that social scientists employ to explore and understand complex social phenomena. The course is structured to provide a solid foundation in the logic of scientific inquiry, the measurement of political concepts, research design, data collection methods, and the principles of statistical inference. Subsequently, the course delves into specific research designs—including experimentation, interviews, social surveys, network analysis, text analysis, and data mining—through the critical examination of scholarly articles utilizing each research design. Each week, we will explore both traditional research designs and the innovative approaches that have emerged in the era of AI-assisted analytics. Students will critically assess how advancements in technology have transformed the ways in which researchers access and collect data, construct measures, design and execute experiments or surveys, and analyze data to address theoretically significant questions.
This course will be available for undergraduate students.
Academic Year 2023-2024
Thomas Wood. "Applied Political Science Computing."
The proposed class, a non-mandatory complement to the existing graduate methods sequence, will meet these lacunae. The class will cover the following topics: (1) The tidyverse tools for data analysis; (2) functional programming, so that students don’t write repetitive, bug-prone code; (3) web scraping and accessing APIs (an especially relevant technology, given the number of language models with accessible APIs)” for punctuation and formatting2; (3) web scraping and accessing APIs (an especially relevant technology, given the number of language models with accessible APIs); (4) an introduction to databases and SQL, and: (5) statistical graphics for publication, and (6) integrating novel statistical and computational techniques into an existing syntactical approach.This course will be available for graduate-level students.
Erin Lin. "Bringing AI Tools to Qualitative Methods."
The goal of this graduate seminar is to explore when qualitative methods are appropriate for a research question and how to competently engage in such research. We will use A.I. tools to automate certain archival tasks, process ethnographic experiences, and test-run interview guides (prior to taking them to the field). Students will apply multiple A.I. tools within this practicum. In partnership with the Ohio State University Archives, Ohio Public Policy Archives, and the Byrd Polar and Climate Research Center Archival Program, students will digitize archival documents to create data sets that we feed into a deep learning tool, training it to spot key words, phrases, and images. In the second half of the semester, students will invest 3-4 hours a week tracing down interviewees, spending time at field sites, doing interviews, and writing up field notes. We will use chatbots to pilot their semi-structured interview guides (preparing students for an array of responses that may require them to be flexible in how they order and ask their questions).This course will be available for graduate-level students.
Zuheir Desai. "Technology in Politics: Efficiency, Accountability, and Fairness."
This course is aimed at students that are broadly interested in the nexus of Economics, Politics, and Technology from both a positive and a normative perspective. The aim is to attract students from multiple majors, including, but not limited to, Computer Science, Economics, Politics Philosophy & Economics (PPE), and Political Science. A major focus will be on evaluating whether and when simultaneously pursuing efficiency, accountability, and fairness goals results in tensions. For example, (when) do we need to sacrifice accountability and/or fairness for efficiency?This course will be available for undergraduate students.
Jan Pierskalla. "Big Data, AI, and Political Control."
Revolutions in information technology have made data about our lives vastly more available. What are the implications of this change for governance? States routinely collect a lot of information about their citizens and make use of it for political ends. From birth registries, censuses, land cadasters, voter rolls, to modern biometric databases, predictive policing, AI and the monitoring of social media, states rely on a varied array of information collection and analysis tools. What determines the kind of information states collect, how they collect it, and how it is used to make policy decisions? Do these tools empower dictators and amplify political control, or they democratize power and strengthen citizens? Who are the winners and losers when it comes to changes in the realm of information technology? The course unpacks the various ways in which states collect information about their citizens, use this information in their decision-making, and associated political conflicts. We will focus on how different types of political regimes—democracies and autocracies—make different choices about how citizen information is collected, used, and how civil society responds.This course will be available for undergraduate students.
Haifeng Huang. "AI and the Politics of Information."
This undergraduate course will explore how AI and algorithmic based technologies affect the flow of political information and the formation of public opinion. Topics will include information aggregation and knowledge generation, misinformation and false news, correcting misinformation, propaganda, censorship, international influence and public diplomacy, electoral campaigns, social robots and social media, and so on. It will examine both how various actors and entities can use and have used AI technologies to generate and shape political messaging, such messaging’s effects on public opinion and other social outcomes, and how such activities can be detected and sometimes countered.This course will be available for undergraduate students.