Why are we averse towards Algorithms? A comprehensive literature Review on Algorithm aversion
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
With technological developments in artificial intelligence, algorithms are increasingly capable to perform tasks that were considered to be unique for humans. However, literature suggests that although algorithms are often superior in performance, users are reluctant to interact with algorithms instead of human agents – a phenomenon known as algorithm aversion. But, as algorithm aversion is attracting scientific attention, empirical findings are inconclusive and papers find the opposite effect of algorithm appreciation. With this literature review, we synthesize evidence from 29 publications with 84 distinct experimental studies to investigate how algorithm characteristics and human agents’ characteristics influence algorithm aversion. We show how algorithm agency, performance, perceived capabilities and human involvement as well as human agents’ expertise and social distance, influence whether users develop algorithm aversion, i.e., choose humans over algorithms, utilize humans’ support more often and evaluate humans’ actions more favourable. Furthermore, we provide a systematic conceptualization of aversion as a biased assessment and develop propositions for future research. With our work, we contribute to algorithm aversion literature and the contemporary discussion on the impact of algorithmic agents on the future of work. We indicate that the emerging literature stream on algorithm aversion is worth considering for human-computer interaction researchers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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