AI in the Courtroom: Enhancing Legal Decision-Making through Predictive Modelling

Surisetty Hima Varshini, Gottimukkala Sarayu Varma, Peeta Basa Pati, Priyanka Prabhakar and Shantipriya Parida
Additional contact information
Surisetty Hima Varshini: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
Gottimukkala Sarayu Varma: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
Peeta Basa Pati: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
Priyanka Prabhakar: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
Shantipriya Parida: ��Silo AI, Helsinki, Finland

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 01, 1-22

Abstract: Legal outcome prediction or legal judgement prediction is the process of making predictions of the possible outcome of a legal proceeding based on its case file. This work focuses on the importance of allowing less privileged or marginalised groups to have better access to law services. This work discusses the implementation of 13 different deep learning models and the results obtained for each of them. The range of models employed encompasses a spectrum from basic perceptron models to more complex convolutional network models and encoder-based models. The models like multi-layer perceptron (MLP), convolutional neural network (CNN) model, BERT, LegalBERT, DistilBERT, RoBERTa and the different combinations of these encoder-based models are implemented. The input to these models is the case files and their corresponding judgement status. Four different classes of petitions and appeals, submitted to the court, were considered to train all the models. The efficiency of these models was tested in terms of various performance metrics like training accuracy, testing accuracy, training loss, validation loss and F1 score.

Keywords: Legal outcome prediction; legal judgement prediction; case file; natural language processing; deep learning; petitions; appeals (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219649224500990
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:jikmxx:v:24:y:2025:i:01:n:s0219649224500990

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219649224500990

Access Statistics for this article

Journal of Information & Knowledge Management (JIKM) is currently edited by Professor Suliman Hawamdeh

More articles in Journal of Information & Knowledge Management (JIKM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().