Using conventional framing to offset bias against algorithmic errors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Prior research has shown that people judge algorithmic errors more harshly than identical mistakes made by humans—a bias known as algorithm aversion. We explored this phenomenon across two studies ( N = 1199), focusing on the often-overlooked role of conventionality when comparing human versus algorithmic errors by introducing a simple conventionality intervention. Our findings revealed significant algorithm aversion when participants were informed that the decisions described in the experimental scenarios were conventionally made by humans. However, when participants were told that the same decisions were conventionally made by algorithms, the bias was significantly reduced—or even completely offset. This intervention had a particularly strong influence on participants’ recommendations of which decision-maker should be used in the future—even revealing a bias against human error makers when algorithms were framed as the conventional choice. These results suggest that the existing status quo plays an important role in shaping people’s judgments of mistakes in human–algorithm comparisons.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it