Proceedings of the 1st ACM workshop on Workshop on AISec
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 1st ACM Workshop on AISec -- AISec '08. The mission of this new workshop is to stimulate increased collaboration between the Security and AI communities. It is our strong belief that such collaboration is the best route towards fully realizing the security and privacy benefits of today's ubiquitous information. The call for papers attracted 20 submissions from Asia, Canada, Europe and the United States. The program committee accepted 7 research papers and 2 position papers covering a variety of topics, including usable access control and authentication, malware and network attack defense and reputation systems. In addition, the program includes two exciting invited talks. The first is by Dr. Chris Clifton of Purdue University; a prominent leader in both the privacy and data mining communities. The second is by Dr. Carl Landwehr, IARPA and University of Maryland. Dr. Landwehr is very well-known for his information assurance research and currently is the Program leader for the National Intelligence Community Information Assurance Research at IARPA, a program with many challenging problems intersecting both Security and AI. We give our heartfelt thanks to the program committee and external reviewers. It is quite challenging crafting a program for a cross-disciplinary conference. The program committee made significant strides in defining this largely new field of research and in soliciting relevant and novel research contributions, and all the reviewers worked very hard to give useful and insightful feedback to the authors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.209 | 0.085 |
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".