Empowering Justice: Exploring The Applicability of AI in The Judicial System
Bibliographic record
Abstract
The constant increase in the number of pending cases in Indian courts has been a cause of concern for the legislative, executive and the judicial wings of the country. To address this issue, several measures have been taken, including pushing for Alternative Dispute Resolution (ADR) mechanisms and eliminating unnecessary laws, but using the recently discovered field of Artificial Intelligence to address this dilemma is still unexplored. A civil or criminal trial can take years to be settled, in contrast to industrialised countries where trials can be completed in a few days. This is due to the issue of a judge scarcity and the rising number of cases being instituted. The end outcome is inefficient and delayed justice delivery, which is not beneficial to any society. Therefore, in addition to traditional answers, creative thinking is required to bring back the efficacy and efficiency of the justice delivery system and ensure its sustainability. Using artificial intelligence to decide legal cases is one such solution. Since India's courts are already undergoing a radical transition as a result of turning digital, the newly-emerging field of study known as "Artificial Intelligence," or "AI," may be able to provide long-term justice delivery and clear the backlog of unresolved cases in unexpected ways. AI systems have already been used by the judiciaries in several developed nations, like the United States and Canada, to support the judges. Artificial intelligence will undoubtedly be a blessing to ensure a sustainable and efficient justice delivery system, as it has already shown its value in a number of industries, including marketing by tracking consumer purchasing patterns, self-driving cars, medical, and transportation. In this research, the benefit of using artificial intelligence to make decisions in court is a workable way to reduce the backlog of cases in India and other jurisdictions while also guaranteeing quick and long-lasting justice delivery systems globally.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".