Integrating Computational Intelligence and Behavioral Sciences for Adaptive Human-Centered Systems
Keywords:
human-centered AI, adaptive systems, behavioral science, sociotechnical systems, fairness, governance, robustness, infrastructure, policyAbstract
Adaptive human-centered systems increasingly mediate high-stakes decisions in health, education, labor, finance, mobility, and public administration. Yet many deployed systems remain cognitively naïve and institutionally under-specified: they optimize measurable proxies while neglecting the behavioral dynamics, social contexts, and governance constraints that determine real-world outcomes. This paper advances a systems-level framework for integrating computational intelligence with behavioral sciences to build adaptive human-centered systems that are robust, sustainable, and socially legitimate. We argue that effective integration requires more than adding “human factors” after model training; it demands architectural coupling between learning components and behavioral theory, explicit modeling of feedback loops and strategic behavior, and governance mechanisms that constrain adaptation within accountable boundaries. We synthesize insights from machine learning, control and reinforcement learning, human-computer interaction, behavioral economics, social psychology, and science and technology studies to articulate core design principles: behaviorally grounded representations, intervention-aware objective design, monitoring of distributional and behavioral drift, and multi-layer oversight spanning technical, organizational, and policy domains. We analyze structural trade-offs among personalization, fairness, transparency, and operational reliability, emphasizing that adaptivity is a socio-technical property shaped by incentives, institutions, and infrastructure. Case illustrations across clinical decision support, educational platforms, and public-sector benefits systems demonstrate how unmodeled behavioral responses can invert intended effects and how governance-aware architectures can mitigate harm. We conclude with a forward-looking research and policy agenda, outlining evaluation paradigms, documentation practices, and regulatory considerations for adaptive systems that shape human behavior at scale.
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