Understanding and Mitigating Gender Bias in Information Retrieval Systems
Bibliographic record
Abstract
Gender bias is a pervasive issue that continues to influence various aspects of society, including the outcomes of information retrieval (IR) systems. As these systems become increasingly integral to accessing and navigating the vast amounts of information available today, the need to understand and mitigate gender bias within them is paramount. This monograph provides a comprehensive examination of the origins, manifestations, and consequences of gender bias in IR systems, as well as the current methodologies employed to address these biases. Theoretical frameworks surrounding gender and its representation in artificial intelligence (AI) systems are explored, particularly focusing on how traditional gender binaries are perpetuated and reinforced through data and algorithmic processes. Metrics and methodologies used to identify and measure gender bias within IR systems are then analyzed, offering a detailed evaluation of existing approaches and their limitations. Subsequent sections address the sources of gender bias, including biased input queries, retrieval methods, and gold standard datasets. Various data-driven and method-level debiasing strategies are presented, including techniques for debiasing neural embeddings and algorithmic approaches aimed at reducing bias in IR system outputs. The monograph concludes with a discussion of the challenges and limitations faced by current debiasing efforts and provides insights into future research directions that could lead to more equitable and inclusive IR systems. This monograph serves as a valuable resource for researchers, practitioners, and students in the fields of information retrieval, artificial intelligence, and data science, providing the knowledge and tools needed to address gender bias and contribute to the development of fair and unbiased information systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".