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Legal Case Outcome Predictor Using AI
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The current judicial system is mostly dependent on man power and there is no automation introduced into the indian judicial system which may help in automating and predicting the legal outcomes of multiple cases according to the given conditions. To resolve these concerns , we developed an AI based Judicial Case Outcome Predictor which uses artificial and convolutional neural networks where inputs of information related to the case is updated into the system which analyses the particular law case and provides with the right outcome of the case and predicts whether it is a bailable or non bailable case. The system is built as a web application where access is restricted to everyone and only a few can access the system and use it in a legal and safe way. This system is our solution towards reducing a lot of human effort and making AI work for you in the judicial sector. We perform a performance analysis test and our system predicts the outcome of the case with around 70% accuracy and can cut down time for analysis of case by 60 percent.
Keywords
AI based judicial case outcome predictor, Artificial Neural Networks, Convolutional Neural Networks. Judicial System
References
[1] G. Roy, S. (2023, July 11). What’s Choking The Indian Judiciary. BQ Prime. Retrieved October 8, 2023.
[2] Pti. (2023, July 20). Cases pending in courts cross 5-crore mark: Govt in Rajya Sabha. The Economic Times.
[3] Sharma, S. K., Shandilya, R., & Sharma, S. (2023). Predicting Indian Supreme Court Judgments, Decisions, or Appeals: eLegalls Court Decision Predictor (eLegPredict). Statute Law Review, 44(1), hmac006.
[4] Ruhl, J. B., Katz, D. M., & Bommarito, M. J. (2017). Harnessing legal complexity. Science, 355(6332), 1377-1378.
[5] Zhu, Mengyuan, et al. "A review of the application of machine learning in water quality evaluation." Eco-Environment & Health (2022).
[6] Berry, Michael W., Azlinah Mohamed, and Bee Wah Yap, eds. Supervised and unsupervised learning for data science. Springer Nature, 2019.
[7] Zhang, Lefei, et al. "Hyperspectral image unsupervised classification by robust manifold matrix factorization." Information Sciences 485 (2019): 154-169.
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How to cite this paper
@article{1708749,
author = {G Balasubramanyam , V Manohar Goud, K Sumanth},
title = {Legal Case Outcome Predictor Using AI},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {2392-2394},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708749.pdf},
abstract = {The current judicial system is mostly dependent on man power and there is no automation introduced into the indian judicial system which may help in automating and predicting the legal outcomes of multiple cases according to the given conditions. To resolve these concerns , we developed an AI based Judicial Case Outcome Predictor which uses artificial and convolutional neural networks where inputs of information related to the case is updated into the system which analyses the particular law case and provides with the right outcome of the case and predicts whether it is a bailable or non bailable case. The system is built as a web application where access is restricted to everyone and only a few can access the system and use it in a legal and safe way. This system is our solution towards reducing a lot of human effort and making AI work for you in the judicial sector. We perform a performance analysis test and our system predicts the outcome of the case with around 70% accuracy and can cut down time for analysis of case by 60 percent.},
keywords = {AI based judicial case outcome predictor, Artificial Neural Networks, Convolutional Neural Networks. Judicial System},
month = {May},
}