Legal Regulation of Algorithms From the Perspective of Interpretability
Bibliographic record
Abstract
The human life in the age of artificial intelligence has undergone tremendous changes. Algorithm technology is widely developed and applied as one of the core technologies of artificial intelligence. However, a series of problems such as algorithm discrimination, algorithm killing, and “information cocoon” caused by unexplainable algorithms represented by artificial neural networks needed to be solved urgently,which forms a risk society. The algorithmic order is gradually “offside” into a new social order, which challenges the existing legal order. Because the existing legal order upholds the neutral value of technology tools and does not pay attention to the legal regulations of technology itself, it cannot make ethical prejudgment of unexplainable algorithms to prevent and control social risks. With the deepening of the “intelligence” of algorithm technology, social risk is expanding. The field of algorithm technology creates interpretable algorithms to respond to social risks. However, due to the lack of legal value and institutional design support, the technological advantages of interpretable algorithms to prevent and control algorithm black boxes, “Offside order” and coping with a risky society cannot be confirmed and guided by law. Therefore, it is an effective way to solve the risks in the age of artificial intelligence by taking the technical critical theory of risk society as the value basis and taking the interpretability of algorithms as a necessary condition for algorithm regulation.
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".