Strategic Best-Response Fairness Framework for Fair Machine Learning
Bibliographic record
Abstract
Discrimination in machine learning (ML) has become prominent as ML is increasingly used for decision-making. Although many "fair-ML" algorithms have been designed to address such discrimination issues, virtually all of them focus on alleviating disparity in the prediction results by imposing additional constraints. Naturally, in response, prediction subjects alter their behaviors. However, the algorithms never consider those behavioral responses. So, even if the disparity in prediction results may be removed, the disparity in behaviors may persist across different subpopulations of prediction subjects. When these biased behavioral outcomes are used for training ML algorithms, they can perpetuate discrimination in the long run. To study this issue, we define a new notion called “strategic best-response fairness” (SBR-fairness). It is defined in a context involving subpopulations that are ex-ante identical and also have identical conditional payoffs. Even if an algorithm is trained on biased data, will it lead to identical equilibrium behaviors of subpopulations? If yes, we define the ML as SBR-fair. We then use this SBR-fairness framework to analyze the property of existing fair ML algorithms. We also discuss how the SBR-fairness framework can inform the design of fair ML algorithms and the practical and policy implications of SBR-fairness.
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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".