Into uncharted waters: Trade secrets law in the AI era
Bibliographic record
Abstract
Artificial intelligence (AI) has many beneficial applications that can improve quality of life, but accountability and transparency can be elusive when problems arise. This paper looks at the tools, systems and measures needed to ensure that when AI systems do more harm than good, their administrators are held accountable. Trade secrets law protects the source code, algorithms and data sets upon which AI systems are built, but they also prevent those harmed by these systems from getting answers. Requests for disclosure, data sharing and due process are being denied in the name of trade secrets protection. Complicating the matter is that trade secrets are treated differently in various jurisdictions.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".