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.
Learn more about our consulting and services:
Analyzing Digital Behavioral Data
Methods, tools, frameworks and infrastructures for analyzing digital behavioral data.
CSS Capacity Building
Talks, tutorials, materials on computational methods for the collection, processing, and analysis of digital behavioral data.
- Conference on Harmful Online Communication (CHOC2023)
Digital Behavioral Data: Datasets
Curated digital behavioral data – datasets for scientific re-use.
- Stier, Sebastian, Arnim Bleier, Haiko Lietz, and Markus Strohmaier. 2018. "Election Campaigning on Social Media: Politicians, Audiences and the Mediation of Political Communication on Facebook and Twitter." Political Communication 35 (1): 50-74. doi: https://doi.org/10.1080/10584609.2017.1334728.
- Hannak, Aniko, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017. " Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr." In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW2017), Portland, OR, USA, February 25 - March 1, 2017, edited by Charlotte P. Lee, Steven E. Poltrock, Louise Barkhuus, Marcos Borges, and Wendy A. Kellogg, 1914-1933. New York: ACM. http://markusstrohmaier.info/documents/2017_CSCW2017_Discrimination.pdf.
- Lamprecht, Daniel, Kristina Lerman, Denis Helic, and Markus Strohmaier. 2017. "How the structure of Wikipedia articles influences user navigation." New Review of Hypermedia and Multimedia 23 (1): 29-50. doi: https://doi.org/10.1080/13614568.2016.1179798.
Political polarization and individualized online information environments: A longitudinal tracking study
NFDI for Data Science and Artificial Intelligence
Dehumanization Online: Measurement and Consequences (Professorinnenprogramm)
Artificial Intelligence without BIAS
The emergence of inequality in social systems