Authors:
Stefanie Urchs
1
;
2
;
Veronika Thurner
1
;
Matthias Aßenmacher
2
;
3
;
Ludwig Bothmann
2
;
Christian Heumann
2
and
Stephanie Thiemichen
1
Affiliations:
1
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences, Munich, Germany
;
2
Department of Statistics, LMU Munich, Munich, Germany
;
3
Munich Center for Machine Learning (MCML), LMU Munich, Munich, Germany
Keyword(s):
Algorithmic Fairness, Academic Visibility, Information Retrieval, Search Engines, Gender Fairness.
Abstract:
Algorithmic systems such as search engines and information retrieval platforms significantly influence academic visibility and the dissemination of knowledge. Despite assumptions of neutrality, these systems can reproduce or reinforce societal biases, including those related to gender. This paper introduces and applies a bias-preserving definition of algorithmic gender fairness, which assesses whether algorithmic outputs reflect real-world gender distributions without introducing or amplifying disparities. Using a heterogeneous dataset of academic profiles from German universities and universities of applied sciences, we analyse gender differences in metadata completeness, publication retrieval in academic databases, and visibility in Google search results. While we observe no overt algorithmic discrimination, our findings reveal subtle but consistent imbalances: male professors are associated with a greater number of search results and more aligned publication records, while female
professors display higher variability in digital visibility. These patterns reflect the interplay between platform algorithms, institutional curation, and individual self-presentation. Our study highlights the need for fairness evaluations that account for both technical performance and representational equality in digital systems.
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