ROBOT JUDGES, ARTIFICIAL INTELLIGENCE, BIAS MITIGATION AND THE FUTURE OF FORENSIC ANALYSIS

Аннотация

This article thoroughly examines the role and prospects of using robot judges and artificial intelligence in the judicial system. It discusses how robot judges can contribute to more efficient and rapid decision-making processes while minimizing human biases that may compromise fairness. However, the article also addresses the risks associated with these technologies, including potential errors, algorithmic uncertainties, a lack of human sensitivity, and issues of accountability. The article highlights the importance of carefully analyzing the legal, ethical, and social challenges inherent in the implementation of artificial intelligence in judicial processes. The author emphasizes that the activities of robot judges should not conflict with fundamental principles such as human rights, fairness, and the rule of law. At the same time, it is noted that these technologies can play a significant role in streamlining judicial processes, providing swift data analysis, and ensuring legal stability. In particular, the article examines foreign practices of employing robot judges and explores the prospects for integrating them into Uzbekistan’s judicial system. In conclusion, the article offers balanced insights into theopportunities and potential risks of using robot judges and artificial intelligence in the future of judicial analysis. It underlines the importance of establishing clear legal frameworks and implementing cautionary measures to ensure that these technologies serve justice effectively withoutundermining human rights and fundamental freedoms. Keywords:robot judges, artificial intelligence, judicial analysis, justice, illegality, impartiality, legal system, digital judicial process, judicial reform, automated decision-making, legal stability, technology and law, algorithmic analysis, reducing the burden of judges, legal security, transparency, responsibility, risk oferror, quality ofjudicial decisions, combating corruption.