Every human will interact with artificial intelligence (AI) in the visible future, directly or indirectly, some for creating products and services, some for research, some for government, some for education, and many for consumption. It is globally acknowledged that the vast majority of people are not sufficiently aware of AI technologies and their potential impacts, and are therefore unprepared for facing the emerging AI wave. This raises critical and urgent questions: Can AI affect human intelligence (HI) adversely? What are the immediate and long-term risks created by AI for HI? How can we develop human performance-supportive AI management policies? What is the role of AI education in preparing humans for interactions with AI? These are open research questions of immense importance and urgency. The critical difference between past scientific industrial revolutions and the current AI spearheaded transformation is that past revolutions replaced human muscle power, while AI has the potential to replace HI in many areas.
Even as the information age augmented human information storage and processing capabilities, it created a massive need for higher human intelligence capabilities such as logic and problem-solving. Current artificial intelligences (‘AIs’, i.e., specific AI applications such as computer vision, adaptive systems, and natural language processing) can solve complex logical problems and perform better than human intelligences in many defined scenarios. A McKinsey Global Institute report estimated that, as compared to the industrial revolution, AI is “happening 10 times faster and at 300 times the scale, or roughly 3,000 times the impact.” The impact is visible: AI can now surpass many human intelligence capabilities, AI can “learn” by itself to improve its own performance in advanced logic problems, as illustratively seen in its capabilities to learn rules and defeat even the world’s best human Chess and Go grandmasters.
This research article collection purposes to explore important issues surrounding AI-HI interaction, with an in-depth examination of AI and HI factors, using multidisciplinary theoretical lens and frameworks, and state of the art methods. AI factors, in the context of human interaction, can include aspects such as big data informatics, optimization, and an array of intelligent learning methods, including machine (supervised and unsupervised), deep, reinforcement, federated, transfer and similar learning methods, applied to any range of AI applications such as autonomous machines, intelligent robots and robotic integrations, personal assistants, smart homes/ building / urban spaces /cities and AI for personal and organizational security or HR management. Human factors can include performance and AI management policy factors related to aspects such as gender prisms, economic class, age, and geography. AI education is a critical component of preparing HI, and we welcome studies that address this preparatory aspect of AI-HI interaction using formal and informal pedagogical formats. We are interested in topics that address compelling issues of human behavior in the context of AI:
• How can AIs augment human performance?
• How can AI governance and policy development enhance human benefit, and reduce AI-driven risks in AI-HI interaction?
• What academic theoretical lens can be used or developed to educate humans to deal successfully with AIs?
• How can AI-driven data analytics and informatics improve various dimensions of human decision-making?
In this research article collection, the focus is on articles and studies that provide theoretical development and empirical findings on three key dimensions of AI-HI interaction: a) human performance augmentation, b) the role of AI governance in moving towards human sensitive AI-HI interaction and c) the dynamics of AI education in preparing people to effectively engage with rapidly expanding AI phenomena. This research article collection invites theoretical, empirical, experimental, and observational studies on AI-HI interaction (including the impact of AI technologies on human psychology and behavior), AI policy, and regulation development. Articles which expound AI-specific education strategies, including gender and minorities sensitive pedagogical studies are welcome.
Certain articles may be considered out of scope for this issue, such as those which deal purely with AIs that completely replace human labor and do not cover any aspects of AI-HI interaction. Pure algorithmic development and other forms of technology analytics papers which does not address human performance, governance, or education may also not be a good fit. In addition to standard quality, rigor, and alignment criteria, articles with unique and innovative contributions which bear the potential to provide extraordinary insights and influence the future significantly will be prioritized.
Every human will interact with artificial intelligence (AI) in the visible future, directly or indirectly, some for creating products and services, some for research, some for government, some for education, and many for consumption. It is globally acknowledged that the vast majority of people are not sufficiently aware of AI technologies and their potential impacts, and are therefore unprepared for facing the emerging AI wave. This raises critical and urgent questions: Can AI affect human intelligence (HI) adversely? What are the immediate and long-term risks created by AI for HI? How can we develop human performance-supportive AI management policies? What is the role of AI education in preparing humans for interactions with AI? These are open research questions of immense importance and urgency. The critical difference between past scientific industrial revolutions and the current AI spearheaded transformation is that past revolutions replaced human muscle power, while AI has the potential to replace HI in many areas.
Even as the information age augmented human information storage and processing capabilities, it created a massive need for higher human intelligence capabilities such as logic and problem-solving. Current artificial intelligences (‘AIs’, i.e., specific AI applications such as computer vision, adaptive systems, and natural language processing) can solve complex logical problems and perform better than human intelligences in many defined scenarios. A McKinsey Global Institute report estimated that, as compared to the industrial revolution, AI is “happening 10 times faster and at 300 times the scale, or roughly 3,000 times the impact.” The impact is visible: AI can now surpass many human intelligence capabilities, AI can “learn” by itself to improve its own performance in advanced logic problems, as illustratively seen in its capabilities to learn rules and defeat even the world’s best human Chess and Go grandmasters.
This research article collection purposes to explore important issues surrounding AI-HI interaction, with an in-depth examination of AI and HI factors, using multidisciplinary theoretical lens and frameworks, and state of the art methods. AI factors, in the context of human interaction, can include aspects such as big data informatics, optimization, and an array of intelligent learning methods, including machine (supervised and unsupervised), deep, reinforcement, federated, transfer and similar learning methods, applied to any range of AI applications such as autonomous machines, intelligent robots and robotic integrations, personal assistants, smart homes/ building / urban spaces /cities and AI for personal and organizational security or HR management. Human factors can include performance and AI management policy factors related to aspects such as gender prisms, economic class, age, and geography. AI education is a critical component of preparing HI, and we welcome studies that address this preparatory aspect of AI-HI interaction using formal and informal pedagogical formats. We are interested in topics that address compelling issues of human behavior in the context of AI:
• How can AIs augment human performance?
• How can AI governance and policy development enhance human benefit, and reduce AI-driven risks in AI-HI interaction?
• What academic theoretical lens can be used or developed to educate humans to deal successfully with AIs?
• How can AI-driven data analytics and informatics improve various dimensions of human decision-making?
In this research article collection, the focus is on articles and studies that provide theoretical development and empirical findings on three key dimensions of AI-HI interaction: a) human performance augmentation, b) the role of AI governance in moving towards human sensitive AI-HI interaction and c) the dynamics of AI education in preparing people to effectively engage with rapidly expanding AI phenomena. This research article collection invites theoretical, empirical, experimental, and observational studies on AI-HI interaction (including the impact of AI technologies on human psychology and behavior), AI policy, and regulation development. Articles which expound AI-specific education strategies, including gender and minorities sensitive pedagogical studies are welcome.
Certain articles may be considered out of scope for this issue, such as those which deal purely with AIs that completely replace human labor and do not cover any aspects of AI-HI interaction. Pure algorithmic development and other forms of technology analytics papers which does not address human performance, governance, or education may also not be a good fit. In addition to standard quality, rigor, and alignment criteria, articles with unique and innovative contributions which bear the potential to provide extraordinary insights and influence the future significantly will be prioritized.
