Fairness revisionism: Reducing discrimination for the future reduces perceived unfairness in the past
Bibliographic record
Abstract
Marginalized groups may face systemic discrimination for generations until concrete advancements in society finally ensure fairer treatment for their members. Although fairness advancements may benefit these groups in the present and future, they do not change the past; they cannot undo the discrimination already experienced by previous generations. However, five studies ( N = 1672) suggest that fairness advancements that benefit a marginalized group may change how its members perceive their own prior experiences with discrimination, leading them to see these experiences as having been fairer compared to when there are no such advancements. We find evidence of this revisionism of unfair past experiences in different historically marginalized groups (women and immigrants) and cultural contexts (U.S., U.K., and China). Critically, fairness revisionism arises even when fairness advancements have no objective impact on individuals themselves, as long as there are benefits for current and future members of their social group. Fairness revisionism does not arise, however, in response to gains for marginalized groups to which one does not belong, nor when individuals assess fairness in other groups' past experiences from an outsider's perspective. Overall, this phenomenon may be a double-edged sword: it may provide peace of mind for those treated unfairly by assuaging the memory of adverse experiences, but may also make discrimination issues in society seem less pressing based on the perspective of victims themselves. • Fairness advancements increase perceived fairness in prior unfair experiences. • This effect arises in response to fairness gains to similar others and not oneself. • This effect does not arise in response to fairness gains to dissimilar others. • Different marginalized groups in different countries showed revisionist tendencies.
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.
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".