The Legal Validity and Evidentiary Value of Artificial Intelligence-Generated Digital Evidence

Abstract

The rapid development of artificial intelligence technologies has led to a fundamental transformation in the nature of digital evidence and its function within judicial proof. Digital evidence is no longer limited to traditional electronic data; rather, in many cases, it has become the product of complex analytical and inferential processes carried out by intelligent systems relying on advanced algorithms such as machine learning, deep learning, and big data analytics. This research aims to examine the legal value of artificial intelligence based digital evidence by analyzing the conceptual and legal frameworks governing this emerging category of evidence and by assessing the extent to which it enjoys probative authority before the courts. It also seeks to highlight the legal challenges raised by such evidence, particularly those related to algorithmic opacity commonly referred to as the “black box” problem and the resulting implications for the right of defense and the principle of adversarial proceedings as fundamental guarantees of a fair trial. Furthermore, the study addresses legal challenges associated with the reliability of AI-based digital evidence, the limits of its susceptibility to judicial discussion and challenge, as well as the issue of determining legal liability for errors arising from the use of such systems. In addition, it reviews contemporary legislative and judicial trends and proposes a set of legal safeguards aimed at achieving a balance between technological advancement and the requirements of procedural justice and the protection of fundamental rights and freedoms.