Examining the Intersection of General Artificial Intelligence and Legal Decision-Making
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
This research paper examines the increasing need for and importance of artificial intelligence (AI) in the legal profession. Along with highlighting its significance, it discusses the benefits and drawbacks of AI in the legal profession. Furthermore, it also analyses the capability of AI to replace human judges in future. Additionally, it investigates the possible problems and impacts on society by integrating AI into the legal profession, such as people's lack of confidence in AI-generated decisions, parties' privacy, unemployment, and transparency. Moreover, it explores how AI can serve as an assistive device rather than a complete replacement for human involvement. It examines countries like China, the USA, and Canada, where AI machines are already being used in their legal proceeding for research, decision-making, and even in some countries, as a substitute for human judges. Furthermore, it investigates the social, ethical and economic effects, and their sufficient solutions, by integrating AI into the judicial system, especially in Pakistan. The effectiveness of AI is compared to human judgments to assess its potential role. Lastly, it provides recommendations for the better implementation of AI tools in Pakistan’s judicial system, suggesting strategic actions to facilitate the integration of AI tools in the legal field.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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