ARTIFICIAL INTELLIGENCE IN THE NEXUS OF LAW: REVIEW OF EXISTING LEGAL FRAMEWORK ACROSS THE GLOBE AND CHALLENGES
Bibliographic record
Abstract
AbstractArtificial Intelligence (AI) is the latest innovation in the field of the science and technology; the frontiers of its application in various sectors are still in nascent stage and are expanding. AI means development of computer systems that are able to perform tasks normally requiring human intelligence. The rise of AI is both obvious and inevitable. The one thing is certain that AI is going to be part of our daily life sooner than we can imagine and in many sectors it has already become inseparable part of our daily transactions. It is well accepted that technology can become boon or curse depending on how it is handled. States and innovators are conscious and aware of the potential misuse of AI Technology. So, there is a consensus prevailing amongst the stakeholders about the necessity of regulating AI Technology in order to prevent its misuse. Misuse of AI Technology breaches various human rights. Leading countries across the world like USA, UK, Canada, France, Australia, Russia, and China have either made legislative framework to regulate AI Technology or are in the process of making it. The Government of India is also keeping close eye on the developments happening in the field of AI Technology. The objectives of this research paper is to highlight the potential threats and benefits of the AI Technology, to make comparative analysis of various legislative framework regulating AI Technology across the globe, what India should take into account while framing laws regarding regulation of AI Technology. The research paper is based on doctrinal research method which has used secondary data available at various science and technology websites, books and legal departments of various governments.Keywords: Artificial Intelligence (AI), Misuse of AI Technology, Human rights, Legislative framework, Comparative analysis.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".