Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness
Stefanie Urchs, Veronika Thurner, Matthias A{\ss}enmacher, Ludwig Bothmann, Christian Heumann, Stephanie Thiemichen

TL;DR
This study examines gender fairness in academic search and retrieval algorithms, revealing subtle biases in visibility and data completeness that reflect societal inequalities and platform influences.
Contribution
It introduces a bias-preserving definition of algorithmic gender fairness and applies it to analyze gender disparities in academic search results and visibility.
Findings
Male professors have more search results and aligned publications.
Female professors show higher variability in digital visibility.
No overt algorithmic discrimination was detected.
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…
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Taxonomy
TopicsDigital Economy and Work Transformation
