Bibliographic record
Abstract
Black-box Artificial Intelligence (AI) systems have achieved state-of-the-art accuracy in many problem domains in recent years. However, the lack of transparency of these systems is a bottleneck in their usage in high-risk applications which make automated decisions on individuals. Trustworthy AI proposes principles such as reliability, validity, privacy, fairness, and explainability among others, to mitigate risks from large-scale AI deployments in such domains. Explainable AI (XAI) is a technique of providing insights into the decision-making process of black-box systems thus enabling transparency. It plays a crucial role in communicating the rationale of automated decisions to relevant stakeholders. Though explainability is a highly desirable requirement, recent research has determined that explanations can introduce new privacy risks in AI systems. Researchers have demonstrated different types of privacy attacks on XAI deployed in production and cloud systems. Despite these risks, currently there is a lack of research into defenses for known privacy attacks in XAI. In this article, we contribute to this gap by proposing a defense mechanism for attribute inference attack on feature-based XAI. We empirically evaluate a well-known privacy preservation technique, namely, additive noise, and show its impact on privacy, explainability and utility. Our findings indicate that additive noise enables privacy while achieving faithful explanations and without compromising model utility.
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 | 0.013 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".