Lex AI: Solution for Governance of Artificial Intelligence in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the third decade of our century, AI is gradually becoming a part of daily life for people. The development of AI-based innovations in different fields such as navigation assistance software, image processing, and chatbots; and AI-based gear that helps paralyzed individuals regain their ability to walk, are convincing examples of how AI is being utilized more and more in daily life. As it develops, legal issues related to the use of AI may also arise, such as ethical issues, legal justice, due process of law, intellectual property, or personal data security. To mitigate legal problems, developing governance over AI is therefore necessary. This research is normative juridical research using statute and conceptual approaches. The legal analysis technique used is the argumentative analysis technique. The study findings indicate that since AI fundamentally differs from coding programs in that it is a dynamic system consisting of a network of algorithms that mimic biological neural networks, a different approach and governance system are required. This can be referred to as Lex Artificial Intelligence, or simply lex AI. Because of its uniqueness, AI governance cannot exclusively use the standard public or private ordering framework. It is then necessary to present lex AI as the sui generis governance with unique regulatory properties that can be paralleled with other laws as a law that complements those other laws.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it