Ethically Aligned AI for Autism and Behavioral Health: An Explainable Federated-Edge Framework for Crisis Management and Workforce Integration

The deployment of Artificial Intelligence (AI) in healthcare is reshaping clinical workflows, yet challenges remain around privacy, explainability, and ethical integration. Autism care is one area where these challenges are particularly acute, as children with autism spectrum disorder (ASD) require continuous monitoring, rapid escalation detection, and personalized interventions. This study proposes an explainable federated-edge AI framework that integrates wearable IoT monitoring, federated privacy-preserving learning, and workforce-aware crisis response modules. Unlike existing centralized approaches, the framework unites federated learning for privacy, edge intelligence for latency reduction, and explainability artifacts for trust-building. A simulation experiment across autism IoT, synthetic workforce, and anomaly datasets demonstrated accuracy improvements of 10%, latency reduction of 55%, and increased clinician trust scores. By embedding ethical AI principles into its architecture, the framework advances both technical performance and human-centered adoption.