Who Gets to be an Expert? The Hidden Bias in Expert Finding
Bibliographic record
Abstract
Ensuring fair and accurate recognition of contributors is critical for the health and growth of online communities; yet Expert Finding (EF) methods on Community Question Answering (CQA) platforms often reinforce existing inequalities. This paper investigates the prevalence and amplification of individual biases in EF methods on (CQA) platforms by introducing a framework which assesses the degree of bias in various attributes in the list of recommended experts. Our work shows that, without corrective measures, automated recommenders risk silencing diverse but less-visible voices; undermining both equity and answer quality. By conducting extensive experiments on multiple Stack Overflow datasets, we analyze how state-of-the-art EF methods reinforce individual bias. Our findings show that EF methods frequently amplify biases, such as prioritizing highly active users over less active participants who may offer higher-quality answers. We also examine how these biases contribute to the recommendation of inaccurate experts, offering a thorough evaluation of the resulting negative impact on the effectiveness and accuracy of CQA platforms. All code and datasets used in this study are publicly available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".