A Systematic Approach to Human-Centered Artificial Intelligence Engineering
Keywords:
human-centered artificial intelligence, sociotechnical systems, system architecture, AI governance, fairness engineering, infrastructure sustainabilityAbstract
The rapid proliferation of artificial intelligence across high-stakes domains has exposed fundamental shortcomings in engineering practices that treat human factors as afterthoughts rather than primary architectural constraints. This paper presents a systematic, infrastructure-oriented framework for human-centered artificial intelligence engineering that integrates technical architecture, organizational governance, sociotechnical feedback loops, and regulatory alignment from the earliest design stages. We argue that human-centered AI cannot be reduced to interface design or usability testing; it requires rethinking entire system stacks, deployment pipelines, monitoring regimes, and institutional accountabilities. Drawing on concepts from large-scale systems engineering, safety-critical infrastructure, and fairness-oriented machine learning, we analyze structural trade-offs between autonomy and oversight, personalization and privacy, model performance and procedural justice, and centralized optimization versus distributed stakeholder control. Through detailed discussion of system architecture patterns, governance models, infrastructure requirements, and lifecycle sustainability, we show how a disciplined human-centered methodology reconfigures core engineering decisions around data collection boundaries, model retraining cadences, explanation interfaces, and contestability mechanisms. The paper further examines how fairness, robustness, and long-term maintainability can be operationalized as system-level properties that demand continuous institutional investment, not merely algorithmic post-processing. In synthesizing these dimensions, we propose a conception of human-centered AI as a sociotechnical infrastructure, subject to evolving societal expectations, regulatory frameworks, and ecological constraints, and offer design principles that align engineering practice with democratic accountability and operational durability.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
