Logistic Regression Modelling of the Influence of Expert Evidence in Criminal Proceedings - Kuhl, Andrea (Essays of Faculty of Law University of Pécs, Yearbook of [year], 2025)
The increasing reliance on forensic expertise in criminal proceedings is attributed to the inherent characteristics of scientific evidence, such as neutrality, objectivity and universality, which provide judges with greater certainty in their decisions. However, Act XXIX of 2016 restricts the role of forensic experts, emphasising that judges must assess cases independently, rather than simply adopting the conclusions of experts. This paper examines the delicate balance between the roles of judges and experts, highlighting the importance of distinguishing between issues of fact and law, and addressing concerns that judges may lose their discretion when faced complex technical issues. The evolving nature of expertise raises questions about its regulation and the implications for judicial decision-making. The relationship between judges and experts in legal proceedings is complex, as experts should provide objective assessments that complement but do not replace judges’ authority. Expert opinions, which combine scientific and subjective elements, can significantly influence judicial decisions, especially in bankruptcy cases. This study uses logistic regression modelling to show how judges tend to favour expert opinions over raw financial data, reflecting a reliance on qualitative factors in their decisions. Two models are proposed to analyze the influence of expert opinions on judicial decisions, with the second model incorporating the expert's opinion leading to a higher predictive power regarding the judge's decisions. The findings suggest that judges may place more trust in expert opinions than in raw financial data, indicating a preference for qualitative assessments over quantitative indicators. The paper concludes by emphasizing the need for a clear distinction between the roles of the expert and the judge.