Artificial Intelligence for Sustainable and Effective Justice Delivery in India
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 and to overcome this problem, various steps are being taken like pressing for Alternative Dispute Resolution (or ADR) mechanisms and scrapping of redundant laws but applying the new found field of Artificial Intelligence to cope up with this conundrum is an area that is still unexplored. India, being the largest democracy in the world with a population of more than 125 crores (1.25 billion), faces the problem of shortage of resources in almost every sector and Indian judiciary is no different. With the problem of shortage of judges and ever increasing rate of institution of cases, the net result is that a civil or a criminal trial takes years to get decided as compared to time taken by developed countries where trial is a matter of a few days. The net result, then, is delayed and ineffective justice delivery which is not very useful for any society. It is, therefore, necessary to think of out of the box solutions, in addition to the conventional ones, to restore the effectiveness and efficiency of the justice delivery system and make the same sustainable. One such solution is putting Artificial Intelligence to use in disposing judicial matters. Since courts in India are already undergoing a transformational change by going digital, the emerging domain of science called ‘Artificial Intelligence’ or ‘AI’ may help in surprising ways to ensure sustainable justice delivery and reduce the backlog of pending cases. 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. Likewise, in India too, court tasks can be identified which can be expedited through the use of intelligent machines. These tasks may range from routine matters such as service of processes to complex ones like evaluation of evidence. This will not only save judicial time of the courts leading to better utilization of public money but may also help in reducing the impact personal biases of the judge in decision making. Of course, trained machines, howsoever intelligent, cannot replace human judges. Nevertheless, these may help the judges in the decision-making process by giving calculated and unbiased opinions and thus ensuring that in the process of handling large number of cases, justice does not become a casualty. In this doctrinal research, the researcher has referred to both primary and secondary sources of data. 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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".