Algorithms and Society

The digitization of society brings about a structural change of the public sphere and the private, in which algorithms – quite literally – play a decisive role. We all interact in and with socio-technical systems, e.g. social media, search engines, online stores, job application platforms, news and information platforms. In these systems, algorithms play a major role in deciding which content, groups, people or institutions are presented or recommended to us and how they are being priorized. Algorithms often take the concrete behavior of users as a starting point and thus create a complex recursive interaction between the operating algorithm and human action or experience. With this, artificial intelligence, algorithms and automated processes create dynamics that might not be perceivable by users but do create social structures that substantially influence our individual lives and society as a whole. Whether or not these consequences are desirable can only be discussed and evaluated if we know precisely how digital technologies, the Web and the algorithms therein shape social structures.

This is why GESIS studies the mechanisms of socio-technical systems in order to understand the social change they bring about and to improve the basis for informed and "good" decisions. We do this through collecting digital behavioral data on societal issues, conducting online experiments to analyze behavioral patterns and their susceptibility in digital environments, and developing analytical tools. One of the most pressing social issues is inequality. Algorithms can reinforce existing social inequality or generate new distortions or discrimination. We investigate how distortions (e.g. gender bias) occur in digital practice and how, on the other hand, algorithms and AI can be used to counteract structural inequality and injustice or misinformation.

  • Ulloa, Roberto, Mykola Makhortykh, Aleksandra Urman, and Juhi Kulshrestha. 2023. "Novelty in News Search: A Longitudinal Study of the 2020 US Elections." Social Science Computer Review 42 (3). doi: https://doi.org/10.1177/08944393231195471.
  • Ulloa, Roberto, and Celina Kacperski. 2023. "Search engine effects on news consumption: Ranking and representativeness outweigh familiarity in news selection." New Media & Society online first. doi: https://doi.org/10.1177/14614448231154926.
  • Ferrara, Antonio, Lisette Espín Noboa, Fariba Karimi, and Claudia Wagner. 2022. "Link recommendations: Their impact on network structure and minorities." In WebSci '22: 14th ACM Web Science Conference 2022, 228-238. New York: Association for Computing Machinery. doi: https://doi.org/10.1145/3501247.3531583.
  • Sen, Indira, Mattia Samory, Claudia Wagner, and Isabelle Augenstein. 2022. "Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, edited by Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz, 4716–4726. Seattle: Association for Computational Linguistics. doi: https://doi.org/10.18653/v1/2022.naacl-main.347.
  • Beytía, Pablo, and Claudia Wagner. 2022. "Visibility layers: a framework for systematising the gender gap in Wikipedia content." Internet Policy Review 11 (1). doi: https://doi.org/10.14763/2022.1.1621.
Title Start End Funder
Political polarization and individualized online information environments: A longitudinal tracking study (POLTRACK)
2022-01-01 2025-08-30 SAW (Leibniz)
NFDI for Data Science and Artificial Intelligence (NFDI4DS)
2021-10-01 2026-09-30 DFG
Dehumanization Online: Measurement and Consequences (Professorinnenprogramm) (DeHum)
2021-01-01 2026-09-30 SAW (Leibniz)
Artificial Intelligence without BIAS (NoBIAS)
2020-01-01 2023-12-31 Horizon 2020
Inequality research (Inequality)
The emergence of inequality in social systems
2017-02-01 2022-02-28 Stiftung

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