Demystifying The Black Box: AI, Algorithmic And Regulatory Evolution
- IJLLR Journal
- Apr 29
- 1 min read
Mr. Saumitra Sharma, Head of Law Department, United University, Prayagraj
Bhumika Agrawal, LL.M., United University, Prayagraj
ABSTRACT
With risk assessment, recidivism forecasting, and punishment prediction, the use of AI in court processes via predictive justice technologies is transforming the decision-making process for the effectiveness and uniformity of the legal system. Predictive justice is the use of AI-enabled technology to analyze vast amounts of data in order to forecast the results of court conflicts. However, since AI is a black box technology, its increasing use in supporting court rulings is also posing serious questions about accountability, transparency, and justice. Our incapacity to comprehend how deep learning algorithms make judgments is known as the black box dilemma. The judicial system is susceptible to systemic prejudices sustained by built-in AI algorithms due to the opacity of these technologies, which creates significant ethical and legal issues such biases, discrimination, and accountability gaps in different AIassisted court rulings. The loophole is that such algorithms are not being sufficiently regulated. This paper reveals all the challenges and problems concerning a black box nature of the technologies in the AI-based judicial system. Solutions to technology problems encouraged in this article include algorithm auditing standards to detect bias, explainable AI (XAI) to ensure transparency, and liability frameworks to hold accountable. To suggest the introduction of international best practices into our legal system, the study also evaluates several global systems. The article views technical innovation as ethically governed in a broader perspective and provides options based on a policy approach to align AI integration to the legal system.
Keywords: Artificial Intelligence, Legal System, Blackbox Dilemma, Integration, Accountability.
