DeepGemini: Verifying Dependency Fairness for Deep Neural Network
Bibliographic record
Abstract
Deep neural networks (DNNs) have been widely adopted in many decision-making industrial applications. Their fairness issues, i.e., whether there exist unintended biases in the DNN, receive much attention and become critical concerns, which can directly cause negative impacts in our daily life and potentially undermine the fairness of our society, especially with their increasing deployment at an unprecedented speed. Recently, some early attempts have been made to provide fairness assurance of DNNs, such as fairness testing, which aims at finding discriminatory samples empirically, and fairness certification, which develops sound but not complete analysis to certify the fairness of DNNs. Nevertheless, how to formally compute discriminatory samples and fairness scores (i.e., the percentage of fair input space), is still largely uninvestigated. In this paper, we propose DeepGemini, a novel fairness formal analysis technique for DNNs, which contains two key components: discriminatory sample discovery and fairness score computation. To uncover discriminatory samples, we encode the fairness of DNNs as safety properties and search for discriminatory samples by means of state-of-the-art verification techniques for DNNs. This reduction enables us to be the first to formally compute discriminatory samples. To compute the fairness score, we develop counterexample guided fairness analysis, which utilizes four heuristics to efficiently approximate a lower bound of fairness score. Extensive experimental evaluations demonstrate the effectiveness and efficiency of DeepGemini on commonly-used benchmarks, and DeepGemini outperforms state-of-the-art DNN fairness certification approaches in terms of both efficiency and scalability.
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.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".