Sociologist, Kiel University, Germany
Social Theory, Algorithmic Sociality, Affect Theory
| Homepage Kiel University, Germany | https://www.st.uni-kiel.de/de/team/professur/prof-dr-robert-seyfert |
| ORCID | https://orcid.org/0000-0002-9667-1970 |
| Beziehungsweisen (Buch) | https://www.velbrueck.de/Open-Access-Publikationen/Beziehungsweisen.html |
| Algorithmic Cultures (Book) | https://www.routledge.com/Algorithmic-Cultures-Essays-on-Meaning-Performance-and-New-Technologies/Seyfert-Roberge/p/book/9781138351899# |
As with other technologies, the issue is not whether students should use AI, but how they should use it.
"students who report retaining decision making power when using AI also tend to report stronger reflective engagement and self-reported critical thinking."
https://www.tandfonline.com/doi/full/10.1080/03075079.2026.2686986#abstract
Neues Bild für unseren Besprechungsraum am Institut für Sozialwissenschaften @kieluni
Schultafel mit bedeutendem historischen Motiv aus den 1950er im neuen Rahmen.
This paper shows that AI hiring systems prefer candidates who used the same AI model to write their CV
Forget tailoring your résumé to the job, just make sure it’s written by the recruiter’s favourite AI model ...
As artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications have not been empirically evaluated. We focus on the hiring context, where job applicants often rely on LLMs to refine resumes, while employers deploy them to screen those same resumes. Using a large-scale controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or produced by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs' self-recognition capabilities. These findings highlight an emerging but previously overlooked risk in AI-assisted decision making and call for expanded frameworks of AI fairness that address not only demographic-based disparities, but also biases in AI-AI interactions.
From 20 to 22 April 1933, the 8th German Sociology Congress @dgsoziologie was scheduled to take place @kieluni
Org. by F. Tönnies, it was intended to discuss bureaucratisation and sociography (statistics).
However, the congress was cancelled due to well-known historical events.
Many thanks to Sebastian Klauke for finding this in the archives.
„Zweifelt nicht an der Wissenschaft, macht sie zu einem Kunstwerk“
Heute jährt sich der 90. Todestag von Ferdinand Tönnies.
Vorgestern Präsentation der digitalen #Briefedition ✉️✉️✉️ zu Ferdinand #Tönnies im wunderschönen Vortragssaal der @landesbibliothek
Hier geht's zur Edition, die weiterhin fleißig mit Briefen gefüttert wird: https://www.ftbe.de/home
@kwi_essen @tcdh #Universität #Trier #Sozialwissenschaftliches #Archiv #Konstanz @dfg_public
Socializing Algorithms Conference
Kiel University, Germany — 7–9 Oct 2026.
Call for abstracts ends 28 Feb 2026
@amoorelouise.bsky.social
https://www.uni-kiel.de/de/zentren/dsc/detailseite/news/socializing-algorithms-conference
Algorithms have become central objects of contemporary societies. They shape how we communicate, work, consume, govern, and imagine possible futures. Yet, despite their pervasiveness, algorithms are not merely technical systems: they are social, political, and cultural objects, embedded in relations of power, shaped by economic incentives, and infused with visions of what society should be. This conference, Socializing Algorithms, brings together scholars from across disciplines to examine algorithms not as isolated technologies, but as co-produced within social practices, institutions, and imaginaries.
Claudia Bamberg