Application of artificial intelligence in criminstics: prospects and risks

The article is devoted to a comprehensive study of the application of artificial intelligence in forensic practice, covering its theoretical foundations, historical development and modern methodologies. The interdisciplinary nature of AI and various approaches to its definition are considered, in particular in the context of European Union legislation. The key stages of the development of AI as a scientific discipline are analyzed, from symbolic AI to the era of deep learning, and their impact on forensics. The main emphasis is placed on the classification and detailed description of AI methods used in forensics, including machine learning (supervised, unsupervised, with reinforcement), deep learning (convolutional and recurrent neural networks), natural language processing (NLP) and computer vision. Specific examples of the application of these technologies for the analysis of visual and textual information, biometric identification, crime prediction and war crimes investigation are considered. The article analyzes the international experience of applying AI in forensics, highlighting both successful cases (the use of biometric systems, acoustic systems for detecting gunshots, analytical tools for searching for criminals), and problematic situations that led to illegal arrests and erroneous convictions due to the bias of algorithms or insufficient accuracy of technologies. Based on international experience, recommendations are formulated for the implementation of AI in forensics in Ukraine. The need for thorough testing, the development of a clear regulatory framework, ensuring the transparency of algorithms, independent expert assessment and human supervision is emphasized. Particular attention is paid to the ethical aspects of the use of AI and its potential impact on human rights. A multifaceted approach to reducing risks and optimizing the use of AI is proposed, including the formation of an ethical and legal framework, ensuring the transparency and accountability of algorithms, assessing and eliminating bias, improving the quality of training data, and establishing interdisciplinary cooperation. The importance of training qualified specialists to work with AI systems is emphasized.