Strategic Best-Response Fairness Framework for Fair Machine Learning
Why this work is in the frame
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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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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it