Representation-based fairness evaluation and bias correction robustness assessment in neural networks
Why this work is in the frame
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Bibliographic record
Abstract
Context: While machine learning has achieved high predictive performance in many domains, decisions may still be biased and unfair regarding specific demographic groups characterized by sensitive attributes such as gender, age, or race. Objectives: In this paper, we introduce a novel approach to assess model fairness and bias correction robustness based on Computational Profile Distance (CPD) analysis with respect to sensitive attributes. Methods: To study model fairness, we quantify the model’s representation difference using the computational profile learned from different subgroups (e.g., male and female) on the individual and group level. To analyze the robustness of bias correction outcomes, we compare the correction suggestions provided based on confidence (i.e., softmax score) and likelihood (i.e., CPD). Results: To demonstrate the potential of the proposed approach, experiments have been performed using 24 models targeting 3 datasets used in previous fairness studies. Our experiments showed that computational profile distributions can effectively address model fairness from a representation perspective. Further, the experiments indicated that confidence-based bias correction decisions can vary largely from likelihood-based ones, and we should take both suggestions into account to obtain robust outcomes. Conclusion: Demonstrated with a set of experiments, our CPD-based approaches can help users build their trust in fairness assessment and bias mitigation of AI decisions, in ethically sensitive domains such as human resources, finance, health, and more.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it