AI-Driven Psychological Profiling on Social Media: Mechanisms, Ethical Breaches, and Regulatory Challenges in Data Inference - Evan Bose; Chaitanya Anil Kumar; Meenakshi N. (Recent Trends in Social Studies, 2025)
This literature review examines AI-driven psychological profiling on social media, analyzing 21 academic studies that focus on machine learning techniques such as supervised learning, deep neural networks, sentiment analysis, and natural language processing. These methodologies infer mental health indicators—such as depression, anxiety, and stress—from users' digital footprints, encompassing linguistic patterns, engagement metrics, and temporal behaviors. While these tools offer potential for early detection of psychological distress, they also raise significant ethical concerns. Key issues include the absence of explicit informed consent, lack of transparency due to algorithmic opacity, data minimization challenges, and the risk of bias amplification. The study critically assesses how biases in training data can disproportionately impact underprivileged populations, resulting in unfair access to interventions and distorted profiling results. The effects of AI-based profiling on autonomy and stigmatization of mental health are also examined. The assessment also emphasizes the absence of worldwide uniformity in regulatory methods and criticizes current legislative frameworks, such as the CCPA and GDPR, which provide scant protections for inferred psychometric data. It suggests multidisciplinary frameworks to improve data sovereignty, privacy, and accountability while promoting more robust enforcement and public monitoring. The review's conclusion highlights the necessity of long-term study and teamwork to fill up knowledge gaps in algorithmic accountability and ethical governance.
Keywords: Mental health; Psychological Profiling; ai ethics; algorithmic opacity; legal protections