Bibliographic record
Abstract
As machine learning becomes prevalent in our daily lives involving a widening array of applications such as medicine, finance, job hiring and criminal justice, one morally & legally motivated need for machine learning algorithms is to ensure fairness for disadvantageous against advantageous groups. Fairness in machine learning aims at guaranteeing the irrelevancy of a prediction output to sensitive attributes like race, sex and religion. To this end, we take an information- theoretic approach using mutual information (MI) which can fully capture such independence. Inspired by the fact that MI between prediction and the sensitive attribute being zero is the "sufficient and necessary condition" for independence, we develop an MI-based algorithm that well trades off prediction accuracy for fairness performance often quantified as Disparate Impact (DI) or Equalized Odds (EO). Our experiments both on synthetic and benchmark real datasets demonstrate that our algorithm outperforms prior fair classifiers in tradeoff performance both w.r.t. DI and EO.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".