Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
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
Deep learning-based medical image classification models have been shown to exhibit race-, gender-, and age-related biases towards certain demographic attributes. Existing bias mitigation methods primarily focus on learning debiased models, which may not guarantee that all sensitive information is removed and usually targets discrete sensitive attributes. In order to address age-related bias in these models, we introduce a novel method called Mitigating Digital Ageism using Adversarially Learned Representation (MA-ADReL), which aims to achieve fairness for age as a sensitive continuous attribute. We propose controlling the mutual information penalty term to reduce the bias for age as a sensitive continuous attribute, and we seek to enhance the fairness without compromising the accuracy. We also employ the fusion of low- and high-resolution inputs to improve the transferable latent representation of medical images. Our method achieves an AUROC of 0.942, significantly outperforming the baseline models while reducing the bias, with an MI score of 1.89. Our experiments on two skin lesion analysis datasets indicate that MA-ADReL can significantly improve the fairness with respect to age-related bias while maintaining high accuracy.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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