Strategic Best Response Fairness in Fair Machine Learning
Bibliographic record
Abstract
While artificial intelligence (AI) and machine learning (ML) have been increasingly used for decision-making, issues related to discrimination in AI/ML have become prominent. While several fair algorithms are proposed to alleviate these discrimination issues, most of them provide fairness by imposing constraints to eliminate disparity in prediction results. However, the use of these fair algorithms may change the behavior of prediction subjects. As such, even though the disparity in prediction results might be removed by fair algorithms, behavioral responses to the use of fair algorithms can still create disparity in behavior which may persist across different groups of prediction subjects. To study this issue, we define a notion called "strategic best-response fairness" (SBR-fair). It is defined in a context that includes different groups of prediction subjects who are ex-ante identical in terms of abilities and conditional payoffs. We utilize a game-theoretic model to investigate whether different types of fair algorithms lead to identical equilibrium behaviors among different groups of prediction subjects. If yes, such an algorithm is considered SBR-fair. We then demonstrate that many existing fair algorithms are not SBR-fair. As a result, implementing these algorithms may impose fairness on prediction results but actually induce disparity between privileged and unprivileged individuals in the long run.
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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 | 0.022 | 0.062 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".