Human-centered AI: The convergence of behavioral science and data operations

Bhaker, Abhimanyu (2025) Human-centered AI: The convergence of behavioral science and data operations. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1901-1910. ISSN 2582-8266

This article explores the emerging paradigm at the intersection of behavioral science and data operations in artificial intelligence systems. As organizations progress beyond technical implementations toward human-aligned AI, a sophisticated synthesis is developing that integrates psychological insights with advanced data frameworks. This integration transforms how AI systems are designed, deployed, and optimized across industries by incorporating the understanding of cognitive biases, decision heuristics, and contextual influences. The article examines how real-time behavioral feedback loops, human-in-the-loop frameworks, and behavioral metrics are revolutionizing traditional data operations. Strategic applications across real estate, financial services, and technology sectors demonstrate how this convergence creates adaptive ecosystems that enhance user experience while driving operational excellence. Implementation challenges including ethical considerations, privacy implications, organizational readiness, and measurement complexity, are analyzed alongside emerging trends such as explainable behavioral AI, cultural adaptation, longitudinal optimization, and collective intelligence frameworks. This human-centered approach represents a critical evolution in AI development—one that balances technological capability with psychological resonance to create systems that not only perform efficiently but meaningfully align with human decision-making processes.