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AI and Social Sciences

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AI and Social Sciences

Last Updated : August 4, 2025
Samuel Git
0 Lessons
0 Enrolled

About Course

This interdisciplinary course explores the profound
intersection of artificial intelligence and social sciences.
It demonstrates how AI can significantly deepen our
understanding of human behavior, complex social
dynamics, and broad societal patterns—from predicting
emerging social trends to analyzing cultural shifts and
optimizing public interventions. Crucially, the course
also emphasizes the application of established social
science theories, insights, and methodologies to
develop more effective, ethical, and responsible AI
systems that inherently account for intricate human and
societal factors.

What Will You Learn?

  • Integrate AI with Social Science Research: Analyze how AI methods, including predictive modeling, network analysis, and causal inference, advance research in fields such as political science, sociology, and behavioral economics.
  • Apply Machine Learning to Social Data: Utilize supervised and unsupervised machine learning techniques (e.g., classification, clustering, regression) for analyzing large-scale social, political, and economic datasets, including public opinion polls, demographic surveys, and economic indicators.
  • Master NLP for Unstructured Social Data: Implement advanced natural language processing (NLP) techniques (e.g., topic modeling, sentiment
  • analysis, named entity recognition) to analyze unstructured data from sources like social media feeds, political discourse, and historical texts.
  • Design Responsible Computational Social Science: Develop rigorous and ethical approaches for computational social science research, addressing critical issues such as data bias, privacy, and algorithmic fairness when studying human populations.
  • Incorporate Social Theory into AI Models: Create AI models that effectively integrate social theories and empirical findings from disciplines like psychology, anthropology, and economics, thereby enhancing their explanatory power and real-world relevance.
  • Navigate Ethical AI in Human Behavior Studies: Critically address complex ethical considerations inherent in using AI to study human behavior, including issues of surveillance, data governance, informed consent, and the potential societal impacts of AI deployment

Course Content

Computational Social Science Foundations
This module delves into the foundational integration of AI and social sciences, exploring emerging research paradigms, quantitative and qualitative methodologies, and big data approaches to social phenomena. Topics include practical aspects of digital trace data collection (e.g., web scraping, API access), rigorous analysis techniques, experimental methods in digital environments, and effective strategies for bridging qualitative insights with quantitative computational research.

Social Network Analysis with AI
This module covers advanced network representation and measures, including community detection algorithms (e.g., Louvain, Girvan-Newman) and models for influence maximization and information diffusion within online and offline networks. It also addresses temporal network analysis to understand evolving relationships, computational models of social contagion and behavioral spread, and AI-driven techniques for identifying polarization and echo chamber formation, alongside intervention design to mitigate negative network effects.

Computational Political Science
This module focuses on applying AI to political science research. It includes automated political text analysis, ideology detection from speeches and social media, and advanced election forecasting models. It further explores computational approaches to analyzing political communication, public opinion mining from diverse sources, predicting legislative behavior, and understanding international relations through data-driven methods

Computational Economics and Sociology
This module covers economic behavior modeling using machine learning algorithms, deep dives into labor market analysis, and advanced techniques for inequality measurement and prediction. Additional topics include agent-based modeling of complex social systems, analysis of mobility and urban patterns using geospatial data, cultural analytics through digital artifacts, digital demography, and sophisticated housing and geographic analysis to understand spatial inequalities

Digital Anthropology and Psychology
This module explores AI for online behavior analysis, practical digital ethnography methods, and personality prediction from digital traces and linguistic cues. It also covers advanced emotion detection, nuanced sentiment analysis across cultures, understanding cultural differences in online expression, the dynamics of identity formation in digital contexts, and assessing psychological well- being in relation to digital media consumption.

Ethics and Responsible Research Design
This module addresses critical research ethics for computational social science, privacy-preserving analysis methods (e.g., differential privacy, federated learning), and challenges of informed consent in digital contexts. It also examines the identification and mitigation of bias in social data and AI algorithms, ensuring research reproducibility and transparency in computational studies, responsible interpretation and communication of findings to diverse audiences, and implementing participatory research approaches with communities.

Capstone Project
Students will conceptualize, design, and execute a significant computational social science research project that applies advanced AI techniques to investigate a specific, real-world social phenomenon or question. Examples include predicting the spread of misinformation on social media, analyzing socioeconomic inequalities using satellite imagery, or modeling voter behavior based on online discourse. Alternatively, students may develop an AI system that explicitly incorporates social science insights to address a practical societal challenge, such as a fair hiring algorithm, a public health intervention prediction tool, or a system for improving civic engagement. All projects must include rigorous considerations for research ethics, methodological soundness, and responsible interpretation and communication of results. Final projects will be presented with comprehensive documentation, code, and compelling visualization of findings.

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