Doctoral researcher, Chair of Ethics of AI and Neuroscience, Technical University of Munich
I study what social media feeds show people about health and the brain, and what people actually watch.
I work on HARMONY, which studies health information on TikTok among young adults, using data they donate from their own accounts. My work combines computational text analysis, surveys and ethics. I am supervised by Marcello Ienca. From October 2025 to January 2026 I was a Fulbright Visiting Scholar at the Stanford Social Media Lab.
Research
Donated TikTok histories linked to surveys: health content and misinformation, served and watched.
How the brain, ADHD and neurotechnology are framed online, and what neuroethics should learn from it.
How movements build narratives, from Querdenken during COVID-19 to radical content in young adults' feeds.
Language models in informed consent, and how AI chatbots answer questions about contested history across languages.
Publications
Sobieska, A. (2026). Preparedness under conditions of polarization: Media logics and the public positioning of neuroethics. Developments in Neuroethics and Bioethics, 235–268.
Starke, G., Sobieska, A., Knochel, K., & Buyx, A. (2026). Epistemic humility meets virtual reality: Teaching an old ideal with novel tools. Journal of Medical Ethics, 52(7), 439–444.
Sobieska, A., & Starke, G. (2025). Beyond words: Extending a pragmatic view of language to large language models for informed consent. The American Journal of Bioethics, 25(4), 82–85.
Sobieska, A., Hampel, M., Weidenspointner, R., Pauli, V., Pan, C., Jun, S.-M., & Gutsmiedl, P. (2025). Decoding the discourse: Analyzing the linguistic features and strategies behind the Querdenken movement's COVID-19 narrative. Health Communication, 40(12), 2591–2601.
Upcoming
1 Oct 2026 · Lightning talk at Trust & Safety Research Conference, Stanford University: Inside the For You Page: algorithmic content exposure, health misinformation and well-being among young adults on TikTok
7–8 Dec 2026 · Poster at New Directions in Social Media Research and Policy, University of Cambridge: TRACE: a four-layer design for reconstructing algorithmic content environments