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Exploring the Power of AI-Driven Decision Making in the Judicial Domain: Case Studies, Benefits, Challenges, and Solutions

Exploring the Power of AI-Driven Decision Making in the Judicial Domain: Case Studies, Benefits, Challenges, and Solutions

Anu Thomas
Copyright: © 2024 |Pages: 15
ISBN13: 9798369306390|ISBN13 Softcover: 9798369306437|EISBN13: 9798369306406
DOI: 10.4018/979-8-3693-0639-0.ch015
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MLA

Thomas, Anu. "Exploring the Power of AI-Driven Decision Making in the Judicial Domain: Case Studies, Benefits, Challenges, and Solutions." Using Traditional Design Methods to Enhance AI-Driven Decision Making, edited by Tien V. T. Nguyen and Nhut T. M. Vo, IGI Global, 2024, pp. 337-351. https://doi.org/10.4018/979-8-3693-0639-0.ch015

APA

Thomas, A. (2024). Exploring the Power of AI-Driven Decision Making in the Judicial Domain: Case Studies, Benefits, Challenges, and Solutions. In T. Nguyen & N. Vo (Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 337-351). IGI Global. https://doi.org/10.4018/979-8-3693-0639-0.ch015

Chicago

Thomas, Anu. "Exploring the Power of AI-Driven Decision Making in the Judicial Domain: Case Studies, Benefits, Challenges, and Solutions." In Using Traditional Design Methods to Enhance AI-Driven Decision Making, edited by Tien V. T. Nguyen and Nhut T. M. Vo, 337-351. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-0639-0.ch015

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Abstract

Artificial intelligence (AI) has emerged as a promising technology capable of revolutionizing the judicial system by improving decision-making processes and reducing human biases. This manuscript explores the transformative potential of artificial intelligence (AI) in the judicial domain. It discusses the existing works in AI for predictive analytics, document analysis, and automated case management, in terms of methodologies and qualitative metrics used in each application. The manuscript also acknowledges the benefits, challenges, and solutions associated with incorporating AI into the judicial domain. These include the need for transparency and explainability in AI algorithms, and the ethical issues surrounding bias and privacy.

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