Abstract

The rapid growth of the gig economy, characterized by flexible, short-term, and task-based employment, has introduced new challenges to worker well-being—particularly in the domain of mental health. Gig workers, including ride-share drivers, freelancers, and delivery personnel, often face financial instability, job insecurity, and social isolation, making them susceptible to anxiety, depression, and burnout. Despite the urgent need for mental health support, gig workers remain underserved due to limited access to traditional healthcare and stigmatization. This research paper explores the emerging role of Artificial Intelligence (AI) in diagnosing mental health conditions among gig workers, offering scalable, low-cost, and accessible alternatives to conventional mental health services.

The study examines AI-driven diagnostic tools such as sentiment analysis, natural language processing (NLP), and machine learning algorithms used in mobile health (mHealth) applications and wearable technologies. It evaluates the effectiveness, accuracy, and ethical implications of these tools in identifying early signs of mental distress. By conducting a cross-platform content analysis and reviewing existing case studies, the paper identifies the most promising AI models tailored to gig work environments.

Findings suggest that AI-powered diagnostics—when designed with contextual sensitivity and ethical safeguards—can significantly improve mental health monitoring among this transient and diverse workforce. However, concerns related to data privacy, algorithmic bias, and the lack of regulatory frameworks remain pressing. The paper concludes by recommending a human-in-the-loop approach, where AI supports rather than replaces mental health professionals, and calls for the co-creation of AI tools with gig workers to ensure cultural, occupational, and emotional relevance.

This research contributes to the intersection of AI, public health, and labor studies, advocating for inclusive mental health innovation in the digital economy.