Wei Xu

Wei Xu, Zaifeng Gao, Marvin Dainoff

Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems, aiming to maximize AI's benefits while mitigating its negative impacts. Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice. To address this gap, this paper proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components: HCAI requirement hierarchy, approach and method taxonomy, process, interdisciplinary collaboration approach, and multi-level design paradigms. A case study demonstrates HCAI-MF's practical implications, while the paper also analyzes implementation challenges. Actionable recommendations and a "three-layer" HCAI implementation strategy are provided to address these challenges and guide future evolution of HCAI-MF. HCAI-MF is presented as a systematic and executable methodology capable of overcoming current gaps, enabling effective design, development, deployment, and use of AI systems, and advancing HCAI practice.

Wei Xu

Traditional sociotechnical systems (STS) theory has been widely used, but there are many new characteristics in the STS environment as we enter the intelligence era, resulting in the limitations of traditional STS. Based on the "user-centered design" philosophy, this paper proposes a new framework of intelligent sociotechnical systems (iSTS) and outlines the new characteristics of iSTS as well as its implications for the development of intelligent systems. Future research of iSTS requires interdisciplinary collaboration, including human factors engineering, this paper finally proposes suggestions from two aspects of human factors engineering methodology and approaches.

Wei Xu, Marvin Dainoff

Artificial intelligence (AI) has brought benefits, but it may also cause harm if it is not appropriately developed. Current development is mainly driven by a "technology-centered" approach, causing many failures. For example, the AI Incident Database has documented over a thousand AI-related accidents. To address these challenges, a human-centered AI (HCAI) approach has been promoted and has received a growing level of acceptance over the last few years. HCAI calls for combining AI with user experience (UX) design will enable the development of AI systems (e.g., autonomous vehicles, intelligent user interfaces, or intelligent decision-making systems) to achieve its design goals such as usable/explainable AI, human-controlled AI, and ethical AI. While HCAI promotion continues, it has not specifically addressed the collaboration between AI and human-computer interaction (HCI) communities, resulting in uncertainty about what action should be taken by both sides to apply HCAI in developing AI systems. This Viewpoint focuses on the collaboration between the AI and HCI communities, which leads to nine recommendations for effective collaboration to enable HCAI in developing AI systems.

Wei Xu, Marvin J. Dainoff, Liezhong Ge et al.

While AI has benefited humans, it may also harm humans if not appropriately developed. The focus of HCI work is transiting from conventional human interaction with non-AI computing systems to interaction with AI systems. We conducted a high-level literature review and a holistic analysis of current work in developing AI systems from an HCI perspective. Our review and analysis highlight the new changes introduced by AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals did not encounter when developing non-AI computing systems. To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals in addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing AI systems. We propose alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems. We also offer strategic recommendations for HCI professionals to effectively influence the development of AI systems with the HCAI approach, eventually developing HCAI systems.