Examining the Intersection of General Artificial Intelligence and Legal Decision-Making
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".