Algorithm Bias and Perceived Fairness: A Comprehensive Scoping Review
Bibliographic record
Abstract
Artificial intelligence (AI)-based algorithms are playing an increasingly prominent role in shaping daily life. However, these algorithms can exhibit biases that exacerbate societal injustices. Such biases have a substantial impact on people's perceptions of algorithmic fairness, yet the precise mechanisms and scope of this phenomenon remain relatively understudied. To address this research gap, a comprehensive scoping literature review is conducted, providing an overview of current research in the field. Subsequently, a novel theoretical model is developed that synthesizes key themes, including algorithm bias, algorithm fairness, perceived fairness, individual characteristics, social characteristics, task characteristics, and technology characteristics. The paper contributes proposing a set of propositions that underscore the critical gaps in the existing literature, contribute to a deeper comprehension of the relationships among the identified themes and their constituent elements, and offer a roadmap for future research in the domain.
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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 | 0.001 | 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 | 0.000 |
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 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".