Artificial Intelligence & Transformation of Indian Judicial System: A Study
Bibliographic record
Abstract
The wheels of justice need to keep rotating. If a spoke gets loose, the entire machinery is taken for a toss and leads to justice not reaching the needy at a time when needed. Due to delay in disposal of cases by the Indian judiciary, AI has become an important talking point. Judiciary in some parts of developed countries like U.S.A and Canada has already deployed AI systems to assist the judges on taking a call on matters like granting of bail and release of offenders on parole. The rapid technological changes and computational power available in AI have made the judiciary embrace it. As Artificial Intelligence has already proved its worth in different fields such as medicine by assisting doctors in conducting surgeries, transportation in the shape of self-driving cars, marketing by tracking consumer buying patterns, etc., it will definitely be a blessing to ensure sustainable and speedy justice delivery system. Therefore, use of Artificial Intelligence in decision making in courts is a viable solution for bringing down the pendency of cases not only in India but also in other jurisdictions and ensuring speedy and sustainable justice delivery systems across the world. However,unambiguously stating that judicial artificial intelligence is never a replacement for human judges is crucial. The study aims to investigate the relationship between AI and Indian judicial system, investigating significant implications, benefits and obstacles when these two areas merge.By emphasizing the improvement of efficiency, precision and ethical concerns this piece explores the potential for AI to bring about transformative changes in different facets of the Indian judicial system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".