Not All Fairness Is Created Equal: Fairness Perceptions of Group vs. Individual Decision Makers
Bibliographic record
Abstract
Drawing on fairness heuristic theory and literature on negative group schemas, we develop and empirically test the idea that, given the exact same decision outcome, people perceive groups to be less fair than individuals when they receive a decision outcome that is unfavorable, but not when they receive one that is favorable or neutral (Studies 1 and 2). To account for this difference in fairness perceptions following an unfavorable outcome, we show that the mere presence of a group as a decision-making body serves as a cue that increases the accessibility of negative group-related associations in a perceiver’s mind (Study 3). Moreover, in a sample of recently laid-off workers—representing a broad range of organizations and demographic characteristics—we demonstrate that those who received a layoff decision made by a group of decision makers (versus an individual) are marginally more likely to perceive the decision as unfair and are marginally less likely to endorse the organization (Study 4). Taken together, the results of all four studies suggest that, in response to the same unfavorable decision outcome, a group of decision makers is often perceived to be less fair than an individual.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".