Strategic Best-Response Fairness Framework for Fair Machine Learning
Bibliographic record
Abstract
This study introduces a framework called “strategic best-response fairness” (SBR-fairness) to address discrimination perpetuated by machine-learning (ML) algorithms. It challenges the conventional focus on fairness solely in prediction results, arguing that this approach ignores how individuals affected by the predictions may alter their behavior in response to algorithmic decisions. The framework considers whether an algorithm, trained on potentially biased data, leads to identical equilibrium behaviors across different subpopulations that are ex ante identical. The study finds that common fair-ML algorithms, such as those relying on color-blindness and demographic parity fairness criteria, do not always achieve SBR fairness. This means that they may not eliminate disparities in effort and outcomes. Equalized odds (EO), however, have been shown to achieve SBR fairness, but they suffer from several practical limitations. The study proposes that SBR fairness is a necessary condition for breaking cycles of discrimination in ML. It also argues that SBR fairness offers a complementary way to assess other fairness criteria and understand behavioral responses. The findings suggest a need for policy and practical focus on designing SBR-fair algorithms that promote equitable outcomes at both the prediction and behavioral level.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".