Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness

2025-08-05

AI Summary

This study investigates gender inequity in academic visibility within algorithmic systems—such as search engines and scholarly databases. It introduces and applies a “bias-preserving” definition of gender fairness, integrating algorithmic, institutional, and individual dimensions. Using heterogeneous academic archive data from German universities, the authors systematically assess gender disparities across three layers: metadata completeness, database retrieval performance, and Google Search visibility. Results reveal that male professors exhibit more stable digital visibility and higher alignment between their scholarly output and algorithmic representations; in contrast, female professors display pronounced volatility—indicating that algorithmic systems implicitly reinforce structural gender inequality even without explicit discrimination. Notably, this is the first study to embed institutional context into algorithmic fairness evaluation frameworks. By empirically demonstrating how technical “neutrality” obscures systemic bias, it provides foundational evidence for critical scholarship on algorithmic governance and informs structural interventions in scholarly infrastructure.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• Analyzing gender fairness in search and retrieval algorithms
• Assessing algorithmic outputs for gender bias reinforcement
• Evaluating digital visibility disparities between male and female professors

Innovation

• Bias-preserving fairness definition for algorithms
• Heterogeneous dataset from German academia
• Analysis of metadata and search visibility