Shodh Sari-An International Multidisciplinary Journal
Vol-03, Issue-01 (Jan-Mar 2024)
An International scholarly/ academic journal, peer-reviewed/ refereed journal, ISSN : 2959-1376
APPLICATION OF MACHINE LEARNING ALGORITHMS IN PREDICTIVE LEGAL ANALYTICS
Ganapathy, Venkatasubramanian
Faculty in Auditing Department, Southern India Regional Council of the Institute of Chartered Accountants of India (SIRC of ICAI), Chennai, Tamil Nadu, Bharat
DOI: https://doi.org/10.59231/SARI7675
Subject: Law and Technology / Artificial Intelligence / Data Science
Page No.330-355
Received: Dec 06, 2023
Accepted: Dec 26, 2023
Published: Jan 01, 2024
Thematic Classification: Machine Learning (ML), Predictive Legal Analytics, Computational Law, Judicial Outcome Prediction, Legal Tech, Algorithm-driven Auditing, Data Mining in Jurisprudence, Automation in Legal Research, Quantitative Legal Analysis.
Introduction
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions based on patterns and statistics. ML applied to various domains, such as natural language processing, computer vision, and recommender systems. One of the emerging applications of ML is in the field of legal analytics, which aims to provide insights and guidance for legal professionals and stakeholders. Predictive legal analytics is a subfield of legal analytics that focuses on using ML to predict the outcomes of legal disputes, such as court cases, arbitrations, or negotiations. Predictive legal analytics can help lawyers and judges to assess the potential legal consequences of their actions, to align their decisions with past precedents, to identify the best strategies for resolving conflicts, and to improve the efficiency and quality of justice delivery. Predictive legal analytics can also help clients and policy makers to understand the legal risks and opportunities involved in their situations, to make informed decisions before initiating or pursuing a legal action, and to evaluate the impact of legal reforms and interventions.
Impact Statement
This research paper on the Application of Machine Learning Algorithms in Predictive Legal Analytics marks a groundbreaking advancement in legal practice. By harnessing the capabilities of machine learning, the study introduces a paradigm shift in legal decision-making. The implementation of sophisticated algorithms enables accurate prediction of case outcomes, risk assessment, and strategic optimization. This transformative approach not only enhances the efficiency of legal processes but also has far-reaching implications for resource allocation, cost reduction, and improved access to justice. The research addresses ethical considerations, ensuring responsible integration of machine learning in the legal domain. With profound implications for legal professionals, policymakers, and marginalized individuals seeking justice, this paper pioneers the way forward in leveraging technology to augment the fairness and efficacy of legal systems.
About Author
VENKATASUBRAMANIAN GANAPATHY
Objective: Career Growth in the teaching profession with academic excellence and pursuit of further
Qualifications and research.
Academic Profile:
M.Phil (Commerce) from The Quaide Milleth College for Men- University of Madras-
Ist Class- (2015-2016).
B.Ed – IGNOU, New Delhi- Distance Mode- 1 st Class – December 2009.
M.Com – Annamalai University – Distance Mode- 1 st Class – May 1995.
D.P.C.S. (Data Preparation and Computer Software) – NCVT Course –Sri Ramakrishna
Mission Computer Centre, Chennai – 1 st Class – April 1993.
B.Com – St. Joseph’s College (Autonomous), Trichy – Bharathidasan University – 1 st
Class – April 1990.
ICWA Inter (Group II) – December 1993.
Academic Experience: 18 + Years
Presently working as a Visiting Faculty in the SIRC of ICAI, Nungambakkam, Chennai,
Tamil Nadu, Bharat
Corporate Experience: 9 Years.
Cite this Article
APA 7th Style: Ganapathy, V. (2024). Application of machine learning algorithms in predictive legal analytics. Shodh Sari-An International Multidisciplinary Journal, 3(01), 330–355. https://doi.org/10.59231/SARI7675
Chicago 17th Style: Ganapathy, Venkatasubramanian. “Application of Machine Learning Algorithms in Predictive Legal Analytics.” Shodh Sari-An International Multidisciplinary Journal 3, no. 1 (2024): 330–355. https://doi.org/10.59231/SARI7675.
MLA 9th Style: Ganapathy, Venkatasubramanian. “Application of Machine Learning Algorithms in Predictive Legal Analytics.” Shodh Sari-An International Multidisciplinary Journal, vol. 3, no. 1, 2024, pp. 330-355, https://doi.org/10.59231/SARI7675.
Statements & Declarations
Review Method: This article underwent a double-blind peer-review process by independent experts in Data Science and Jurisprudence to evaluate the technical rigor of the machine learning models and their practical viability in predicting judicial outcomes and legal risk.
Competing Interests: The author, Venkatasubramanian Ganapathy, declares that there are no financial, personal, or professional conflicts of interest that could have inappropriately influenced the research findings or the interpretation of the algorithmic data presented.
Funding: This research was conducted through the academic support of the Southern India Regional Council (SIRC) of the Institute of Chartered Accountants of India (ICAI), Chennai. No specific external grants were received for this study.
Data Availability: The analysis is based on supervised learning datasets and legal case law repositories used to train predictive models. Detailed explanations of the Natural Language Processing (NLP) techniques and classification algorithms are provided within the manuscript.
License: Application of Machine Learning Algorithms in Predictive Legal Analytics © 2024 by Venkatasubramanian Ganapathy is licensed under CC BY-NC-ND 4.0. This work is published by the International Council for Education Research and Training (ICERT).
Ethics Approval: As this study involves the analysis of public legal records and algorithmic modeling without direct intervention with human subjects, it was deemed exempt from formal institutional ethical review, while strictly adhering to data privacy and ethical AI development standards.
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