Summary of Convolutional Neural Networks Can Achieve Binary Bail Judgement Classification, by Amit Barman et al.
Convolutional Neural Networks can achieve binary bail judgement classification
by Amit Barman, Devangan Roy, Debapriya Paul, Indranil Dutta, Shouvik Kumar Guha, Samir Karmakar, Sudip Kumar Naskar
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the dearth of machine learning applications in India’s legal domain, particularly concerning lower courts and regional languages. They develop a Convolutional Neural Network (CNN) architecture for Hindi legal documents and achieve an overall accuracy of 93% in predicting bail outcomes, surpassing the benchmark set by Kapoor et al. (2022). This breakthrough has implications for improving legal decision-making and enhancing access to justice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fill a gap in using machine learning for legal issues in India. It creates a special kind of computer program called a Convolutional Neural Network (CNN) that works well with Hindi documents from the courts. The team tested this program by trying to predict whether someone would get bailed out or not, and it was very good at doing so – 93% accurate! This is a big improvement over what others have done before. |
Keywords
* Artificial intelligence * Cnn * Machine learning * Neural network
