How to overcome algorithm aversion: Learning from mistakes
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 When consumers avoid taking algorithmic advice, it can prove costly to both marketers (whose algorithmic product offerings go unused) and to themselves (who fail to reap the benefits that algorithmic predictions often provide). In a departure from previous research focusing on when algorithm aversion proves more or less likely, we sought to identify and remedy one reason why it occurs in the first place. In seven pre‐registered studies, we find that consumers tend to avoid algorithmic advice on the often faulty assumption that those algorithms, unlike their human counterparts, cannot learn from mistakes, in turn offering an inroad by which to reduce algorithm aversion: highlighting their ability to learn. Process evidence, through both mediation and moderation, examines why consumers fail to trust algorithms that err across a variety of prediction domains and how different theory‐driven interventions can solve the practical problem of enhancing trust and consequential choice in algorithms.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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