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 sits at the forefront of many on-going advances in a variety of learning tasks. Despite its supremacy in accuracy under benign environments, Deep learning suffers from adversarial vulnerability and privacy leakage (e.g., sensitive attribute inference) in adversarial environments. Also, many deep learning systems exhibit discriminatory behaviors against certain groups of subjects (e.g., demographic disparity). In this paper, we propose a unified information-theoretic framework to defend against sensitive attribute inference and mitigate demographic disparity in deep learning for the model partitioning scenario, by minimizing two mutual information terms. We prove that as one mutual information term decreases, an upper bound on the chance for any adversary to infer the sensitive attribute from model representations will decrease. Also, the extent of demographic disparity is bounded by the other mutual information term. Since direct optimization on the mutual information is intractable, we also propose a tractable Gaussian mixture based method and a gumbel-softmax trick based method for estimating the two mutual information terms. Extensive evaluations in a variety of application domains, including computer vision and natural language processing, demonstrate our framework's overall better performance than the existing baselines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| 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