Artificial Intelligence for Early Detection of Substance Abuse Risk: A Behavioral and Social Data Approach.

ABSTRACT : Substance abuse is a major public health issue that often goes unnoticed until it leads to serious harm for individuals and communities. This study investigates how Artificial Intelligence (AI) can help detect early warning signs of substance abuse risk by analyzing behavioral and social data. Using publicly available datasets, we develop machine learning models that assess factors such as lifestyle habits, psychological indicators, and online activity patterns. The goal is to create a non-invasive, data-driven tool for early intervention, which could help prevent long-term health and social consequences. Additionally, we examine the ethical challenges of using sensitive data, including privacy concerns, algorithmic fairness, and transparency in AI decision-making. Our findings suggest that AI has significant potential to assist healthcare providers, social workers, and policymakers in designing more effective and personalized prevention strategies. By identifying at-risk individuals earlier, this approach could reduce the societal and economic burden of substance abuse.