Forensic Investigation of Artificial Intelligence Systems

This paper examines the emerging need for the forensic investigation of artificial intelligence (AI) systems. As AI-based tools and algorithms become increasingly integrated into high-stakes domains like law enforcement and cybersecurity, critical challenges arise in forensically auditing these technologies to ensure reliability, accountability, and transparency. This paper extensively discusses the ability of AI technologies, such as machine learning, to automate analyses, data extraction, and evidence classification efficiency. However, there are many issues in the forensic analysis of AI systems, such as transparency in AI decision-making processes, possible biases, adversarial threats, and vulnerabilities that can interrupt investigations. This study employs a systematic literature review methodology for lately published literature, screening and incorporating 15 sources that are evaluated in this emerging area of AI in forensics. Themes obtained from the sources by thematic analysis are pertinent to both current AI applications in forensic investigation and ongoing impediments in AI system evaluation. Practice implications include the requirement for more robust, dependable AI for accountability and formalized ethical and legal frameworks to make sure that algorithmically produced evidence is reliable and admissible. Overall, taking into account the obtained knowledge, more interdisciplinary research is crucial for the successful integration of AI and forensic science.