Computational Social Science Seminar: Tina Eliassi-Rad, Northeastern University
The Truth of the Matter in the Age of Generative AI
Abstract:
We live in an age where algorithmic and human behaviors are deeply intertwined. Generative AI tools, such as ChatGPT, Claude, and Gemini, have seen widespread adoption, yet their effects on us and our influence on them remain poorly understood. These tools often contribute to epistemic instability in society. For example, generative AI tools are not experts in any field and are prone to falsehoods (also known as hallucinations) and adversarial attacks, yet they are often treated as experts. The relationship between a person and a generative AI tool is further complicated by the sense of familiarity users experience. The lack of effective oversight and accountability for this technology exacerbates these problems. How can we govern a technology that is evolving so rapidly? A digitally savvy public is an essential part of the solution.
Papers:
[1] C. Wagner, M. Strohmaier, A. Oltearnu, E. Kıcıman, N. Contractor, T. Eliassi-Rad. Measuring Algorithmically Infused Societies. Nature, 595: 197-204, 2021. https://doi.org/10.1038/s41586-021-03666-1
[2] G. Savcisens, T. Eliassi-Rad, L.K. Hansen et al. Using Sequences of Life-events to Predict Human Lives. Nature Computational Science, 4: 43–56, 2024. https://doi.org/10.1038/s43588-023-00573-5
[3] Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models. UNESCO, March 2024. https://unesdoc.unesco.org/ark:/48223/pf0000388971
[4] D. Pedreschi, L. Pappalardo, E. Ferragina et al. Human-AI Coevolution. Artificial Intelligence, 339: 104244, 2025. https://doi.org/10.1016/j.artint.2024.104244
[5] G. Savcisens, T. Elaissi-Rad. The Trilemma of Truth in Large Language Models. In NeurIPS 2025 Mechanistic Interpretability Workshop, December 2025. https://arxiv.org/abs/2506.23921
[6] S. Dies, C. Maynard, G. Savcisens, T. Eliassi-Rad. Representational and Behavioral Stability of Truth in Large Language Models. arXiv, 2511.19166v3, January 2026. https://arxiv.org/abs/2511.19166
About the CSS Seminar Series:
The University of Michigan School of Information’s Computational Social Science seminar series brings together a vibrant and diverse community of scholars whose cutting-edge research in information science, computer science and the social sciences aims to broaden our understanding of important social and technological issues.
The organizer for the Winter 2026 series is UMSI assistant professor Sabina Tomkins.
This seminar is sponsored by the John Seely Brown Technology and Society Lecture Gift Fund.
Please RSVP for attendance and lunch planning.
Featured Speaker
Tina Eliassi-Rad
Northeastern University
Tina Eliassi-Rad is the Inaugural Joseph E. Aoun Professor at Northeastern University. She is also an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that a member of the technical staff at Lawrence Livermore National Laboratory. She earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Tina works at the intersection of AI and network science and is interested in the impact of science and technology on society. Her algorithms have been integrated into systems used by governments, industry, and open-source software. Tina received an Outstanding Mentor Award from the U.S. Department of Energy's Office of Science in 2010, became an ISI Foundation Fellow in 2019, was named one of the 100 Brilliant Women in AI Ethics in 2021, received Northeastern University's Excellence in Research and Creative Activity Award in 2022, was awarded the Lagrange Prize in 2023, and was elected Fellow of the Network Science Society in 2023.
Contact: [email protected]