How we work

Interdisciplinary builders

We build interaction prototypes, data science pipelines, and experimental technology inspired by theories from HCI, psychology, political science, and science & technology studies.

Quantitative-empirical

To produce reliable empirical insights, we use methods such as controlled experiments, data analysis, audits, and user studies, involving more than 20,000 participants to date.

People- and impact-focused

We like to work with policymakers, journalists, and industry to translate research into practice. Our work has been cited in international press and in testimony before the U.S. Senate.

News & Press

The project ClaimGuard, a collaboration between our lab, HU Berlin, CAIS, Uni Bremen, and codetekt, evaluates how dialogues with AI can support lay people’s fact-checks
  • Bauhaus Journal| 2026
Reports on research — including ours — showing how AI writing tools can make people’s language and expression more similar to one another.
  • Nature News| 2026
Covers our study showing that AI autocomplete suggestions can subtly shift people’s opinions on social issues.
  • Science News| 2026

Our people

Nelson Navajas
Nelson
Navajas
PhD Studenthe/him
Mirjam Nowotny
Mirjam
Nowotny
PhD Studentshe/her
Isabella Lee Arturo
Isabella
Lee Arturo
PhD Associateshe/her
Maximilian Eder
Maximilian
Eder
MSc Researcherhe/him
Julian Klüber
Julian
Klüber
MSc Researcherhe/him

Publications

Highlights
  • Science Advances| 2026 |
    • Williams-Ceci, S., Jakesch, M., Bhat, A., Kadoma, K., Zalmanson, L., & Naaman, M.

AI writing assistants powered by large language models are increasingly used to make autocomplete suggestions to people as they write text. Can these AI writing assistants affect people’s attitudes in this process? In two large-scale preregistered experiments (N = 2582), we exposed participants writing about important societal issues to an AI writing assistant that provided biased autocomplete suggestions. When using the AI assistant, the attitudes participants expressed in a posttask...

  • PNAS 120.11| 2023 |
    • Jakesch, M., Hancock, J. T., & Naaman, M.

Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were...

Recent work
  • ACM CHI| 2026 |
    • Bhat, A., Aubin Le Quéré, M., Naaman, M., & Jakesch, M.
  • iScience, 28(12)| 2025 |
    • Purcell, Z. A., Jakesch, M., Dong, M., Nussberger, A. M., & Köbis, N.

Teaching

Lecture + Exercise

Social Data Analysis

How can we collect and analyze digital data about human behavior? This course covers the pipeline of social data science research: from collecting survey data and wrangling digital...

Project

Responsible AI

What does it mean to build AI systems that are fair, transparent, and accountable? In this project students learn about the principles and frameworks guiding responsible AI development....

Supervision

Thesis Research

We supervise bachelor’s and master’s theses on topics at the intersection of computational methods and social science. Our linked thesis guide offers an overview of how we approach...