An Integrative Perspective on Algorithm Aversion and Appreciation in Decision-Making
Bibliographic record
Abstract
People have conflicting responses for support from algorithms or humans in decision-making. On the one hand, they fail to benefit from algorithms due to algorithm aversion, as they reject decisions provided by algorithms more frequently than those made by humans. On the other hand, many prefer algorithmic over human advice, an effect of algorithm appreciation. However, currently, we lack a shared understanding of these constructs’ meaning and measurements, resulting in a lack of theoretical integration of empirical findings. Thus, in this research note, we conceptualize algorithm aversion as the preference for humans over algorithms in decision-making and analyze approaches in current research to measure this preference. First, we outline the implications of focusing on a specific understanding of algorithms as computational procedures or as embedded in material or nonmaterial objects. Then, we classify four decision configurations that distinguish individuals’ evaluations of algorithms, human advisors, their own judgments, or combinations of these. Consequently, we develop a classification scheme that provides guidance for future research to develop more specific hypotheses on the direction of preferences (aversion vs. appreciation) and the effect of moderators.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".