PNAS · 2026
Kobi Hackenburg
PhD Candidate, University of OxfordResearch Scientist, UK AI Security Institute
About
I am a computational social scientist studying persuasion, communication, and artificial intelligence. My research combines human experiments with hands-on work building and training AI systems to answer fundamental questions about the dynamics of individual and collective influence. My work has been published in journals including Science, PNAS, and Nature Human Behaviour, and covered in venues such as The New York Times, The Washington Post, The Atlantic, The Guardian, and MIT Technology Review.
Currently, I am finishing my PhD in social data science at the University of Oxford as a Clarendon Scholar, advised by Chris Summerfield and Helen Margetts. Alongside my PhD, I work as a Research Scientist at the UK AI Security Institute, where I lead research on AI persuasion and human influence. I am also a Visiting Fellow at the LSE Data Science Institute.
News
- Oct 2026 Published a new paper — on AI persuading people to take real-world political actions — in PNAS
- Jun 2026 Posted a new preprint, my third PhD chapter, on AI systems out-persuading expert humans
- Jun 2026 Appointed Visiting Fellow at the LSE Data Science Institute
- Jun 2026 Named a finalist for the Falling Walls Science Breakthrough of the Year (Social Sciences & Humanities)
- Dec 2025 Published my second PhD chapter — on the levers of political persuasion with conversational AI — in Science
- Mar 2025 Published my first PhD chapter — on scaling laws for static political persuasion with language models — in PNAS
- Jul 2024 Joined the UK AI Security Institute as a Research Scientist
- Jun 2024 Published my MSc thesis — on political microtargeting with large language models — in PNAS
- Oct 2023 Joined The Alan Turing Institute as a doctoral researcher
- Sep 2023 Started my PhD in social data science at the University of Oxford, funded by a Clarendon Scholarship
Selected Publications
Preprint · 2026 · under review at Nature
AI systems out-persuade expert humans
Selected press: The Washington Post, Science Magazine
Science · 2025
The levers of political persuasion with conversational artificial intelligence
Selected press: The New York Times, The Washington Post, The Atlantic, The Guardian, MIT Technology Review, Scientific American, New Scientist, Science Magazine
PNAS · 2025
Scaling language model size yields diminishing returns for single-message political persuasion
PNAS · 2024
Evaluating the persuasive influence of political microtargeting with large language models
All Publications
* denotes equal contribution. See also Google Scholar.
Preprints & Working Papers
- Hackenburg, K., Wagner, C., Hewitt, L., Tappin, B. M., Saunders, E., Kirk, H. R., Margetts, H., & Summerfield, C. (2026). AI systems out-persuade expert humans. Under review at Nature. Data + Code
- Röttger, P., Hackenburg, K., Kirk, H. R., & Summerfield, C. (2026). Measuring and mitigating persona distortions from AI writing assistance. Under review at Nature.
- Yang, M., Casper, S., Stray, J., Li, J., Jones, C., Gausen, A., Jacques, N., Christian, B., Gyevnár, B., Kirk, H. R., He, Z., Zhao, D., Looi, S. S., Levy, J., Hackenburg, K., Seger, E., Kowal, M., Malonza, M., Hewitt, L., Lin, H., Sap, M., Hadfield-Menell, D., Costello, T., Rabbany, R., Godbout, J.-F., Rand, D. G., Kasirzadeh, A., Pennycook, G., Bengio, Y., & Pelrine, K. (2026). AI epistemic risks: Emerging mechanisms & evidence. SSRN working paper.
- Summerfield, C., Anselmetti, R., Avin, S., Cheung, V., Eccles, T., Gausen, A., Hackenburg, K., et al. (2026). How is AI impacting people? Under review at Nature Human Behaviour.
- Summerfield, C., Luettgau, L., Dubois, M., Kirk, H. R., Hackenburg, K., Fist, C., Slama, K., Ding, N., Anselmetti, R., Strait, A., Giulianelli, M., & Ududec, C. (2025). Lessons from a Chimp: AI “Scheming” and the Quest for Ape Language.
Peer-Reviewed Publications
2026
- Hackenburg, K.*, Hewitt, L.*, Wagner, C., Summerfield, C., & Tappin, B. M. (2026). AI can persuade people to take political actions. Proceedings of the National Academy of Sciences, 123(42), e2611368123. Data + Code
- Luettgau, L., Kirk, H. R., Hackenburg, K., Bergs, J., Davidson, H., Ogden, H., Siddarth, D., Huang, S., & Summerfield, C. (2026). Conversational AI increases political knowledge as effectively as self-directed internet search. PNAS Nexus, 5(10), pgag305.
- Belluzzo, U., Hackenburg, K., Kirk, H. R., Hale, S. A., & Röttger, P. (2026). This House Debates AI: Evaluating a language model in Oxford-style debates against human experts. Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC).
- Röttger, P., Hinck, M., Hofmann, V., Hackenburg, K., Pyatkin, V., Brahman, F., & Hovy, D. (2026). IssueBench: Millions of realistic prompts for measuring issue bias in LLM writing assistance. Transactions of the Association for Computational Linguistics, 14, 318–340.
2025
- Hackenburg, K.*, Tappin, B. M.*, Hewitt, L., Saunders, E., Black, S., Lin, H., Fist, C., Margetts, H., Rand, D. G., & Summerfield, C. (2025). The levers of political persuasion with conversational artificial intelligence. Science, 390(6777), eaea3884. Data + Code
- Summerfield, C., Argyle, L. P., Bakker, M., Collins, T., Durmus, E., Eloundou, T., Gabriel, I., Ganguli, D., Hackenburg, K., Hadfield, G. K., Hewitt, L., Huang, S., Landemore, H., Marchal, N., Ovadya, A., Procaccia, A., Risse, M., Schneier, B., Seger, E., Siddarth, D., Sætra, H. S., Tessler, M. H., & Botvinick, M. (2025). The impact of advanced AI systems on democracy. Nature Human Behaviour, 9(12), 2420–2430.
- Hackenburg, K., Tappin, B. M., Röttger, P., Hale, S. A., Bright, J., & Margetts, H. (2025). Scaling language model size yields diminishing returns for single-message political persuasion. Proceedings of the National Academy of Sciences, 122(10), e2413443122. PDF Data + Code
- Hackenburg, K.*, Ibrahim, L.*, Tappin, B. M., & Tsakiris, M. (2025). Comparing the persuasiveness of role-playing large language models and human experts on polarized US political issues. AI & SOCIETY, 1–11. Data + Code
- Williams, A. R., Burke-Moore, L., Chan, R. S. Y., Enock, F. E., Nanni, F., Sippy, T., Chung, Y.-L., Gabasova, E., Hackenburg, K., & Bright, J. (2025). Large language models can consistently generate high-quality content for election disinformation operations. PLOS ONE, 20(3), e0317421.
- Von Mohr, M., Hackenburg, K., Tanzer, M., Fotopoulou, A., Campbell, C., & Tsakiris, M. (2025). A leader I can (not) trust: understanding the path from epistemic trust to political leader choices via dogmatism. Politics and the Life Sciences, 44(1), 88–107.
2024
- Hackenburg, K. & Margetts, H. (2024). Evaluating the persuasive influence of political microtargeting with large language models. Proceedings of the National Academy of Sciences, 121(24), e2403116121. PDF Data + Code
- Hackenburg, K. & Margetts, H. (2024). Reply to Teeny and Matz: Toward the robust measurement of personalized persuasion with generative AI. Proceedings of the National Academy of Sciences, 121(43), e2418817121.
- Pretus, C., Javeed, A. M., Hughes, D. R., Hackenburg, K., Tsakiris, M., Vilarroya, O., & Van Bavel, J. J. (2024). The Misleading count: an identity-based intervention to counter partisan misinformation sharing. Philosophical Transactions of the Royal Society B, 379(1897), 20230040. PDF Data + Code
2023
- Hackenburg, K., Brady, W. J., & Tsakiris, M. (2023). Mapping moral language on US presidential primary campaigns reveals rhetorical networks of political division and unity. PNAS Nexus, 2(6), pgad189. PDF Data + Code