Neuroscience, Genetics, Education, and AI: Charting New Frontiers in Understanding Human Behaviour and Criminal Responsibility

Abstract

The eternal dilemma: how much does genetics influence human behaviour? Be it so-called normal or pathological. How much does education and existential experiences influence human behaviour? "The decisional spaces", that is, I am free to choose between letting myself go to the homicidal impulse or I am conditioned by the pathology. Can new research finally provide us with a valid answer or are we still far from conclusive scientific certainties? This work explores how neuroscience, genetics, education, and AI collectively shape human behaviour and influence criminal responsibility. It examines the extent to which biological predispositions, such as gene variants and structural brain anomalies, interact with environmental factors and educational experiences to determine both typical and pathological behaviours. By introducing the concept of "decisional spaces", the study questions whether individuals freely choose their actions or are constrained by neurobiological and psychological factors. Drawing on recent neuroimaging and behavioural studies, the analysis highlights key findings on impulse control and decision-making processes, including evidence that specific brain structures and functions may predispose individuals to criminal behaviour. These insights are juxtaposed with research on how educational interventions and life experiences can modify behaviorual outcomes, potentially mitigating the impact of inherent predispositions. Furthermore, the work reviews legal cases, particularly rulings from the Italian Criminal Court of Cassation up to 2019, that demonstrate an evolving recognition of neurobiological influences in assessing criminal responsibility. These cases underscore the legal challenges of balancing scientific evidence with traditional notions of free will and accountability. In addition to integrating findings from neuroscience and genetics, this study also explores the burgeoning role of AI. Advanced algorithms and machine learning techniques are increasingly used to process complex datasets from clinical, behavioural, and forensic research. AI-driven analyses can reveal patterns that inform both individualised risk assessments and broader policy decisions, offering a promising avenue for enhancing the objectivity and precision of legal evaluations. Ultimately, this work provides a comprehensive, multidisciplinary overview that not only advances scientific understanding but also informs legal debates. It aims to contribute to a more nuanced framework for evaluating criminal responsibility, one that reflects the sophisticated dynamics between biology, education, and emerging technologies in crafting human behaviour.