Detecting and Mitigating Bias in Data Using Machine Learning with Pre-Training Metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the proposed algorithm to detect the bias from the datasets and to mitigate the bias in the datasets was observed. The consequences of this work shows that not only bias in a model can be decreased without forfeiting model performance rate, but improving the performance. Class imbalance, KL divergence, sample disparity and Kolmogorov-Smirnov (KS) are the pre-training metrics used in the work. Each metric is given weightage and the features are detected based on the maximum weightage. The model is trained to learn the unbiased data and shows the significant improvement in the performance of the system. ROC curve, False Positive Rate and False Negative Rate is used for bias trade-off. The comparison between FPR and FNR before mitigating bias and after mitigating bias is performed and its results are significantly improved.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.001 |
| 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 it