Bibliographic record
Abstract
Abstract Scholarship on when and why humans are willing to rely on algorithms rather than other humans has made substantial progress in recent years, although virtually all such research is based on Western, educated, industrialized, rich, and democratic (WEIRD) research participants. This limits efforts to understand the cultural generalizability of attitudes toward algorithms. In this paper, I study algorithm aversion among participants from over 30 countries on all inhabited continents, thereby significantly increasing the diversity of this field's knowledge base. Furthermore, I leverage this diversity to test a theoretically derived prediction: that perceived corruption makes algorithmic decision‐making more appealing. I find that participants who are born or raised in countries with high levels of perceived corruption are much less averse to algorithmic decision‐making (or, in some studies, are not at all algorithm averse), relative to those from countries with low perceived corruption. Furthermore, experimentally varying corruption salience causes a decrease in algorithm aversion. I explore mechanisms and boundary conditions of these effects and discuss the implications in the context of algorithms that can both increase and decrease injustice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".