FairDETOCS: An approach to detect and connect unfair models
Bibliographic record
Abstract
In the dawn of the digital age, Artificial Intelli-gence and decision-making systems have become omnipresent, completely shaping various aspects of our daily lives. Unfortu-nately, as these systems grow more complex and influential, they also raise significant ethical and societal concerns. As machine learning and AI continue to evolve, ensuring fairness in these systems has become increasingly important. The challenge lies in making models fair, necessitating algorithms that mitigate biases, particularly intersectional biases. Numerous studies have highlighted the lack of intersectional bias treatment in current methodologies. Thus, we propose in this paper a novel approach named FairDetocs. This method incorporates fairness metrics to measure intersectional biases and employs a reweighting algorithm to mitigate them. The process begins with the initial evaluation of biases, followed by data adjustment through the reweighting algorithm, and concludes with a re-evaluation of biases to ensure effective reduction. We base our study on the Adult Income dataset, modified with Canadian statistics. Our results demonstrate a noticeable attenuation of biases, showcasing the effectiveness of FairDetocs in promoting fairness in machine learning models. Overall, our exploratory study provides a significant contribution to the field of fairness in machine learning and sets the stage for further research on this topic.
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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 | 0.030 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".