AI for Improving Justice Delivery: International Scenario, Potential Applications & Way Forward for India
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
Judiciary in India has been under tremendous pressure due to large number of cases pending at various levels. From time to time, several initiatives have been taken to reduce the backlog of pending cases in the courts. One of these is leveraging information and communication technology (ICT). Under this initiative (called e-Court), ICT solutions have been developed and deployed. This has led to visible improvement in the productivity. Even during the Covid-19 pandemic, the courts in India have been functioning. However, the number of pending cases has still been growing due to various reasons including increase in economic activities, awareness in the public and ease of access to the courts. The present work explores the possibility of using artificial intelligence (AI) in the processes to improve the justice delivery in India. A comprehensive literature survey was conducted to review the applications developed and deployed in this domain in other countries viz. Australia, Brazil, Canada, China, UK, and USA. Based on this, it identifies the gaps and suggests a spectrum of potential applications possible in Indian context. The article suggests a way forward for facilitating development and deployment of AI applications in this domain in India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it