CLOSED Call For Papers: Hybrid Human–Artificial Intelligence

Computer seeks articles for an upcoming special issue.
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Submissions Due: 29 February 2020

Articles Due: CLOSED

Publication Date: August 2020

Computer plans an August 2020 special issue on Hybrid Human–Artificial Intelligence.

Recent advances in increased computation power enabled by cloud computing–coupled with emerging machine learning and statistical methods, in addition to the availability of big data generated by the Internet of Things (IoT), mobile devices, and social networks–have led to both a great leap in the need and progress of artificial intelligence (AI) technologies and applications. AI techniques have shown huge potential, as demonstrated in the Alpha Go competition. Some AI applications have reached industrial standards and offer real-world deployment opportunities such as face-recognition-based security checks or ticketless boarding. No doubt, AI will increasingly enable machines to respond to even more complicated situations and handle complex problems, and AI-enabled applications, such as precision medicine, intelligent transport, and smart cities, to name but a few. It is expected that AI will become the driving engine for the future economy and society. Nevertheless, there are two compelling issues from a technical and ethical perspective relating to the foreseen AI-enabled industrial innovation and intelligent world that remain unsolved.

On the technical side, though powerful learning algorithms are now able to extract and establish models and patterns from large-scale datasets–where the machines perform better than humans–there are problems of explanability and interpretability due to the opaque nature of some learning algorithms. In addition, there is a gap from the knowledge learned (models and patterns) to problem-solving, namely the capabilities of reasoning and inference compared with application business logic for decision support or application specific functions. Existing AI techniques are still struggling in reasoning and inference. This is one clear area where humans significantly outperform machines and therefore remains a challenge for AI to become a daily technology and a driver for economy development. On the ethical side, the vision that AI-enabled machines one day may replace humans, and the significant progress made in the past several years, also brings growing concerns that AI may overtake human intelligence, or in the worst situation spiral out of control to destroy human society. For example, it is difficult to imagine what would happen if autonomous drones or self-driving cars can make decisions by themselves, and if they can learn and evolve by themselves. This is not just fiction or assumption, it is reality.

Clearly, human intelligence and AI each have their own strengths and weaknesses. Machines are effective and efficient for discovering implicit knowledge or hidden patterns from large-scale data, whereas humans are good at conducting cognitive analysis such as reasoning, inference, and making instinct judgments by taking into consideration dynamic and multiple factors. They do not have to be competitive, mutually exclusive, or one dominating/replacing another. One way to address the above two issues is to marry the strengths and mitigate the weaknesses of human intelligence and AI, making them work in collaboration and cooperation. To this end, the latest research endeavor, coined as Hybrid Human–Artificial Intelligence (H-AI), has emerged. H-AI is dedicated to investigating models, methods, technologies, and systems that enable and support the synergy, symbiosis, and augmentation of human and artificial intelligence. This provides a promising approach to the technical and ethical challenges–humans and machines can each focus on what they are good at, meanwhile humans are still largely in control in decision making.

This special issue aims to 1) draw attention of relevant communities to this emerging and promising research area; 2) provide a forum to disseminate the latest views and research results relating to the theories and practice of H-AI; 3) inspire and stimulate relevant research and technology development; 4) help guide and forge research communities for future H-AI research.

We expect to realize the co-adaptation and co-optimization between humans and AI with H-AI, in order to achieve a more intelligent world. We seek articles that are directed towards H-AI, including innovative concepts, significant techniques, and new applications. Specific topics include but are not limited to the following:

  • H-AI concepts, models, and paradigms
  • H-AI system architecture and realization methodologies
  • H-AI interaction and collaboration modelling and evolution
  • Autonomy, control, and management methods and mechanisms of individual H-AI elements
  • Learning, adaptation, and synchronization between human and artificial intelligence
  • Joint reasoning and decision-making methods in H-AI systems
  • H-AI applications in smart cyber physical systems
  • Ethical considerations and responsible research on H-AI systems
  • H-AI system performance assessment metrics and evaluation methods

Only submissions that describe previously unpublished, original, state-of-the-art research and that are not currently under review by a conference or journal will be considered.

There is a strict 6,000-word limit (figures and tables are equivalent to 300 words each) for final manuscripts. Articles should include no more than 20 references. Authors should be aware that Computer cannot accept or process papers that exceed this word or reference limit.

Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding a focus on theory, mathematics, jargon, and abstract concepts.

All manuscripts are subject to peer review on both technical merit and relevance to Computer’s readership. Accepted papers must be well written and understandable, as the level of editing will be a light copyedit. For accepted papers, authors will be required to provide electronic files for each figure according to the following guidelines: for graphs and charts, authors must submit them in their original editable source format (PDF, Visio, Excel, Word, PowerPoint, etc.); for screenshots or photographs, authors must submit high-resolution files (300 dpi or higher at the largest possible dimensions) in JPEG or TIFF formats.

Please direct any correspondence before submission to the guest editors at

Liming Chen, De Montfort University, UK

Huansheng Ning, University of Science and Technology Beijing, China

Chris D. Nugent, Ulster University, UK

Zhiwen Yu, Northwestern Polytechnical University, China=

For author guidelines and information on how to submit a manuscript electronically, visit For full paper submission, please visit