Applying human-centered AI in developing effective human-AI teaming: A perspective of human-AI joint cognitive systems

Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teamingComment:

An HCAI Methodological Framework: Putting It Into Action to Enable Human-Centered AI

Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems

Enabling Human-Centered AI: A Methodological Perspective

Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI to humans and avoid potential adverse impacts. While HCAI continues to influence, the lack of guidance on methodology in practice makes its adoption challenging. This paper proposes a comprehensive HCAI framework based on our previous work with integrated components, including design goals, design principles, implementation approaches, interdisciplinary teams, HCAI methods, and HCAI processes. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe this systematic and executable framework can overcome the weaknesses in current HCAI frameworks and the challenges currently faced in practice, putting it into action to enable HCAI further

New paradigmatic orientations and research agenda of human factors science in the intelligence era

Our recent research shows that the design philosophy of human factors science in the intelligence age is expanding from "user-centered design" to "human-centered AI". The human-machine relationship presents a trans-era evolution from "human-machine interaction" to "human-machine/AI teaming". These changes have raised new questions and challenges for human factors science. The interdisciplinary field of human factors science includes any work that adopts a human-centered approach, such as human factors, ergonomics, engineering psychology, and human-computer interaction. These changes compel us to re-examine current human factors science's paradigms and research agenda. Existing research paradigms are primarily based on non-intelligent technologies. In this context, this paper reviews the evolution of the paradigms of human factors science. It summarizes the new conceptual models and frameworks we recently proposed to enrich the research paradigms for human factors science, including a human-AI teaming model, a human-AI joint cognitive ecosystem framework, and an intelligent sociotechnical systems framework. This paper further enhances these concepts and looks forward to the application of these concepts. This paper also looks forward to the future research agenda of human factors science in the areas of "human-AI interaction", "intelligent human-machine interface", and "human-AI teaming". It analyzes the role of the research paradigms on the future research agenda. We believe that the research paradigms and agenda of human factors science influence and promote each other. Human factors science in the intelligence age needs diversified and innovative research paradigms, thereby further promoting the research and application of human factors science.Comment: 26 pages, in Chinese languag

Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming

The rapid advancements in artificial intelligence (AI) have led to a growing trend of human-AI teaming (HAT) in various fields. As machines continue to evolve from mere automation to a state of autonomy, they are increasingly exhibiting unexpected behaviors and human-like cognitive/intelligent capabilities, including situation awareness (SA). This shift has the potential to enhance the performance of mixed human-AI teams over all-human teams, underscoring the need for a better understanding of the dynamic SA interactions between humans and machines. To this end, we provide a review of leading SA theoretical models and a new framework for SA in the HAT context based on the key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA) framework unifies human and AI behavior, and involves bidirectional, and dynamic interaction. The framework is based on the individual and team SA models and elaborates on the cognitive mechanisms for modeling HAT. Similar perceptual cycles are adopted for the individual (including both human and AI) and the whole team, which is tailored to the unique requirements of the HAT context. ATSA emphasizes cohesive and effective HAT through structures and components, including teaming understanding, teaming control, and the world, as well as adhesive transactive part. We further propose several future research directions to expand on the distinctive contributions of ATSA and address the specific and pressing next steps.Comment: 52 pages,5 figures, 1 tabl

مقایسه تصاویر دو بعدی و سه بعدی در یادگیری درس علوم اعصاب

مقدمه: استفاده از تصاویر از ضروریات آموزش در پزشکی است و بررسی تاثیر نوع تصویر بر یادگیری فراگیران می تواند به آموزش موثر کمک نماید. هدف از مطالعه حاضر مقایسه تصاویر دو بعدی و سه بعدی در یادگیری درس علوم اعصاب است. روش: در این مطالعه مورد شاهدی تعدادی تصاویر دوبعدی و سه‌بعدی سیستم عصبی به 61 نفر دانشجو در دو نوبت ارائه شد و یادگیری آزمودنی‌ها با استفاده از آزمون چهار گزینه‌ای مورد بررسی قرار گرفت. جهت تحلیل داده‌ها از آزمون تی وابسته و تحلیل پراکنش استفاده شد. نتایج: یافته‌ها نشان داد که تصاویر سه‌بعدی موجب یادگیری بهتر می‌شوند( آماره پی کمتر از 01/0). کارایی دختران بهتر از پسران (آماره پی کمتر از 05/0) و ارتباطی بین جنس و نوع تصویر وجود نداشت(آماره پی بیشتر از 05/0). نتیجه گیری: تصاویر سه‌بعدی به دلیل جزئیات بیشتر در حافظه فعال ماندگارترند. تفسیر علائم و نشانه ها در تصاویر دو بعدی می-تواند موجب اضافه بار بر حافظه کاری و کاهش کارایی فراگیر گردد

Personality Openness Predicts Driver Trust in Automated Driving

Maintaining an appropriate level of trust in automated driving (AD) is critical to safe driving. However, few studies have explored factors affecting trust in AD in general, and no study, as far as is known, has directly investigated whether driver personality influences driver trust in an AD system. The current study investigates the relation between driver personality and driver trust in AD, focusing on Level 2 AD. Participants were required to perform a period of AD in a driving simulator, during which their gaze and driving behavior were recorded, as well as their subjective trust scores after driving. In three distinct measures, a significant correlation between Openness and driver trust in the AD system is found: participants with higher Openness traits tend to have less trust in the AD system. No significant correlations between driver trust in AD and other personality traits are found. The findings suggest that driver personality has an impact on driver trust in AD. Theoretical and practical implications of this finding are discussed

Healthy waterways and ecologically sustainable cities in Beijing-Tianjin-Hebei urban agglomeration (northern China) : challenges and future directions

The cities across the northern dry region of China are exposed to multiple sustainability challenges. Beijing-Hebei-Tianjin (BTH) urban agglomeration, for example, experiences severe water shortages due to rapidly expanding urban populations, industrial use, and irrigation-intensive agriculture. Climate change has further threatened water resources security. Overuse of water resources to meet the demand of various water sectors has far-reaching health and environmental implications including ecosystem sustainability. Surface water and groundwater pollution present public health risks. Despite the extraordinary policies and efforts being made and implemented by the Government of China, the BTH region currently lacks coordination among stakeholders leading to poor water governance. Consultation among scientists, engineers and stakeholders on regional water security issues is crucial and must be frequent and inclusive. An international symposium was held in Shijiazhuang in early November 2019 to identify some of the key water security challenges and scope of an idealized future eco-city in the region by developing a sustainability framework. This work drew on experiences from across China and beyond. Scientists agree that integration of science, technology, and governance within an appropriate policy framework was particularly significant for combating the issue of water insecurity, including in the region's newly developed city, Xiong'an New Area. An emerging concept, “Healthy Waterways and Ecologically Sustainable Cities” which integrates social, ecological and hydrological systems and acts as an important pathway for sustainability in the 21st century was proposed in the symposium to tackle the problems in the region. This high level biophysical and cultural concept empowers development goals and promotes human health and wellbeing. The framework on healthy waterways and ecologically sustainable cities can overcome sustainability challenges by resolving water resource management issues in BTH in a holistic way. To implement the concept, we strongly recommend the utilization of evidence-based scientific research and institutional cooperation including national and international collaborations to achieve the Healthy Waterways and Ecologically Sustainable Cities goal in the BTH in future. This article is categorized under: Water and Life > Conservation, Management, and Awareness. © 2020 Wiley Periodicals LLC. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Giri Kattel, Jessica Reeves and Kim Dowling” is provided in this record*