Integrating artificial intelligence in forensic science

Thesis. The thesis of this article is to explore the integration of artificial intelligence (AI) methodology in forensic science. An assessment of the potential implications of AI for improving investigative processes and outcomes will also be addressed. Concept. The concept focuses on exploring the application of AI technologies in ar- eas of science such as analysing evidence recognising patterns and supporting decision- making systems. The article emphasises the practical use of AI algorithms in investiga- tions. on exploring how artificial intelligence technologies can be implemented in various aspects of forensic science. Results. An analysis of existing literature and case studies shows that integrating AI into forensic science can improve efficiency, accuracy, and objectivity in investigations. AI tools can automate tasks analyse datasets and identify patterns that might be challeng- 16 “ABoUT The INTeRNeT” - TheoRy ing for humans to detect. However utilising AI in forensic science poses challenges like algorithms, privacy issues with data handling, and the necessity for oversight. Further- more, when employing intelligence for inquiries it is essential to prioritise transparency, and accountability and uphold integrity in decision-making processes. Originality. This article adds to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along, with ethical concerns. By merging research findings and offering perspectives on emerging trends we can gain insights, into the impact of AI on advancing investigations in the years ahead. Additionally, the theoretical structure presented here establishes a foundation for research studies and practical implementations, within the field of forensic science. Keywords: Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AI