Artificial Intelligence Circumvents Identity-Driven Biases in Source Selection
Bibliographic record
Abstract
Social identity profoundly shapes whom people choose as information sources, constraining exposure to diverse perspectives. While people are motivated to seek accurate information, they systematically avoid outgroup sources even when group membership is irrelevant to the task at hand. Here we investigate whether artificial intelligence (AI) can circumvent these identity-driven biases in source selection. In Study 1, a nationally representative sample of American adults (n = 1,054) preferred AI over human sources when seeking information about political conflicts. In Study 2 (n = 284), an incentivized political fact-checking experiment revealed that participants preferred AI sources over outgroup (d = 0.470) and even ingroup (d = 0.230) partisan sources, despite recognizing they were of equal competence. In Study 3 (n = 277), using an identity-irrelevant shape categorization task, participants only preferred AI over outgroup sources (d = 0.191), with no difference between AI and ingroup sources. Computational modeling revealed that these preferences emerge through selectively accumulated evidence against partisan advisors during deliberation, rather than differences in priors. These findings suggest that AI's perceived neutrality enables it to bypass identity-based discrimination. These results highlight the potential of AI to reduce echo chambers and broaden epistemic exposure by serving as an identity-neutral conduit for information acquisition.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".