Keywords
forensic examination
expert workload
EU AI Act
automated distribution
anonymization
anti-corruption mechanisms
How to Cite
Abstract
The article examines the current issues regarding the workload of state forensic institutions in Ukraine and the uneven distribution of expert proceedings. Based on the analysis of statistical data and current departmental instructions, a systemic imbalance in the workload of forensic experts across various Research Institutes of Forensic Expertise (RIFEs) has been identified. An innovative “Blind Load-Balancing” model based on artificial intelligence (hereinafter – AI) algorithms is proposed. The proposed module involves a three-tier filtering process: verificationof specialization, analysis of current workload, and randomized selection of an executor from among the least burdened specialists. Particular attention is paid to the concept of depersonalization (anonymization) of the expert at the appointment stage, which strengthens the principles of independence and objectivity in forensic activities. The study correlates the proposed model with the requirements of the European Artificial Intelligence Act (EU AI Act 2024/1689) regarding high-risk AI systems. It is substantiated that the implementation of such a system will ensure compliance with reasonable timeframes of proceedings and minimization of corruption risks in the appointment of examinations. The purpose of the study is a comprehensive analysis of the current state of forensic examination appointments and expert studies, as well as an examination of the prospects for appointing forensic examinations and expert studies using AI. The methodological basis of the work consists of general scientific and specialized research methods, in particular, formal-logical, systemic-structural, comparative-legal, and the method of analysis and generalization of scientific sources and forensic practice. Scientific novelty: for the first time, a “Blind” randomized distribution of examinations model (Blind Load-Balancing Model) is proposed, based on the presumption of equivalent qualification of experts and the use of two legally significant selection criteria — specialty andworkload. This formulates a new approach to the appointment of examinations in law enforcement practice, which is not described in current legislation and scientific literature. The conclusions emphasize the possibility and feasibility of applying the distribution of forensic examinations and expert studies using AI, which is possible and necessary in conditions of a significant workload on forensic experts.
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