Mirko Casu

We explored the intricate interplay of biological, social, and psychological factors contributing to substance use disorder (SUD) and co-occurring psychiatric symptoms. Drug misuse is a global concern, with increasing prevalence rates affecting mental well-being and safety. The spectrum of SUD includes polysubstance users, posing challenges for treatment and associated health outcomes. Various psychoactive substances like cannabis, hallucinogens, opioids, and stimulants impact addiction vulnerability, with marijuana being widely used globally. Gender differences in SUD prevalence have narrowed, with women escalating drug consumption rapidly once initiated. Age disparities in substance use highlight regional variations among adolescents. Comorbidities with psychiatric symptoms are common, with mood and anxiety disorders frequently observed. This study aimed to analyze factors influencing SUD development and maintenance to inform prevention strategies and treatment recommendations. By conducting a systematic search of databases, sixty articles were reviewed, revealing diverse methodologies and geographic locations. Biological factors, including neurotransmitter systems like endocannabinoid and dopaminergic systems, play a significant role in addiction. Genetic and neurobiological factors contribute to cannabis addiction susceptibility. Social factors such as childhood experiences and parenting styles influence substance use behaviors. Psychological factors like personality traits and mental health conditions interact with SUD development. Understanding these multifaceted interactions is crucial for designing effective interventions to address the complexities of SUD and co-occurring psychiatric symptoms.

This paper examines the impact of cognitive biases on decision-making in forensics and digital forensics, exploring biases such as confirmation bias, anchoring bias, and hindsight bias. It assesses existing methods to mitigate biases and improve decision-making, introducing the novel “Impostor Bias”, which arises as a systematic tendency to question the authenticity of multimedia content, such as audio, images, and videos, often assuming they are generated by AI tools. This bias goes beyond evaluators’ knowledge levels, as it can lead to erroneous judgments and false accusations, undermining the reliability and credibility of forensic evidence. Impostor Bias stems from an a priori assumption rather than an objective content assessment, and its impact is expected to grow with the increasing realism of AI-generated multimedia products. The paper discusses the potential causes and consequences of Impostor Bias, suggesting strategies for prevention and counteraction. By addressing these topics, this paper aims to provide valuable insights, enhance the objectivity and validity of forensic investigations, and offer recommendations for future research and practical applications to ensure the integrity and reliability of forensic practices.

Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential benefits in improving mental and emotional well-being, addressing specific mental health conditions, and facilitating behavior change. However, challenges related to usability, engagement, and integration with existing healthcare systems were identified. AI chatbots hold promise for mental health interventions, but widespread adoption hinges on improving usability, engagement, and integration with healthcare systems. Enhancing personalization and context-specific adaptation is key. Future research should focus on large-scale trials, optimal human–AI integration, and addressing ethical and social implications.

While the challenge of distinguishing AI-generated from real images is widely acknowledged, the specific cognitive biases that systematically shape human judgment in this domain remain poorly understood. It is particularly unclear how a general awareness of AI capabilities fosters novel biases, like a pervasive skepticism (“impostor bias”), and how this interacts with established phenomena like “automation bias”. This study addresses this gap by providing the first quantitative analysis of how these two biases operate across five distinct experimental variants designed to test the context-dependency of human perception. Through a mixed-methods study with 746 participants, we demonstrate that human authentication accuracy hovered around chance levels (ranging from 47.0% to 55.5%). However, our analysis provides robust evidence for the systematic operation of cognitive biases. We validate the presence of “impostor bias” through a consistent pattern of higher doubt for AI-generated images and confirm “automation bias” through significant opinion changes following algorithmic suggestions. Our findings reveal that these biases are not uniform across populations: gender was a consistent predictor of automation bias, with males in all five variants showing a significantly stronger and more consistent tendency (Cohen’s d = 0.254–0.683) to be influenced by algorithmic suggestions. In contrast, age and academic background had minimal and highly localized effects. Furthermore, we identified a significant interaction between experimental stimuli and performance over time, isolating a pronounced fatigue effect to a single questionnaire variant where accuracy progressively declined (by approximately 1.7% per trial). By integrating human feedback with Grad-CAM visualizations, we confirm a divergence between human holistic evaluation and the localized focus of machine learning models. These findings carry direct implications for policy, as discussed within the context of the European AI Act, and inform the design of human–AI systems and media literacy programs aimed at mitigating these critical cognitive vulnerabilities.