Propagation of societal gender inequality by internet search algorithms
Bibliographic record
Abstract
Humans increasingly rely on artificial intelligence (AI) for efficient and objective decision-making, yet there is increasing concern that algorithms used by modern AI systems produce discriminatory outputs, presumably because they are trained on data in which societal biases are embedded. As a consequence, their use by human decision makers may result in the propagation, rather than reduction, of existing disparities. To assess this hypothesis empirically, we tested the relation between societal gender inequality and algorithmic search output and then examined the effect of this output on human decision-making. First, in two multinational samples ( n = 37, 52 countries), we found that greater nation-level gender inequality was associated with more male-dominated Google image search results for the gender-neutral keyword “person” (in a nation’s dominant language), revealing a link between societal-level disparities and algorithmic output. Next, in a series of experiments with human participants ( n = 395), we demonstrated that the gender disparity associated with high- vs. low-inequality algorithmic outputs guided the formation of gender-biased prototypes and influenced hiring decisions in novel scenarios. These findings support the hypothesis that societal-level gender inequality is recapitulated in internet search algorithms, which in turn can influence human decision makers to act in ways that reinforce these disparities.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".