About this Research Topic

Background

Human-Centric Computing (HCC) covers a diversity of computational science and engineering technology with one principle – Human beings should be treated as an end, not a means. Grounded on this principle, the definition and coverage of HCC are self-explanatory: HCC strives to develop computer systems that fit human capabilities and practices by exploiting and improving computer science, social science, management studies, and artificial intelligence (AI) programming methods. The realizations of HCC indeed pervade every corner of our daily lives, ranging from automated drug discovery (micro-level) to intelligent interfaces and user modeling (middle-level) and all the way to the epidemic tracking systems (macro-level). Examples are numerous. These advancements benefit human beings by saving labor and manpower, prioritizing system efficiency and convenience, and advocating social good. While a worrying trend has been observed very recently: HCC methods are more and more developed in a data-driven fashion,
Which inevitably leads to the so-called “embedded bias”. That is, the data collected from the society in which inequity exists tend to include bias. Such bias persists and eventually escalates in the resultant computer systems or models, which may, in turn, hurt our society by, for example, making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating underrepresented and/or marginalized groups based on culture, gender, ethnic, and education background. To counter this embedded bias, we call for new HCC developments that bring together the recent data-driven systems and cognitive algorithms, which possess the capabilities of incorporating social justice and consciousness and equity-awareness into the computer and human-centric robotic systems. We envision such new HCC developments can be on the ground, forward-facing, while demonstrating care over profit.

This research topic aims to bridge the gaps between the various disciplines involved in designing and implementing human-centric computing/robotic system. By championing this research topic, we hope to present a comprehensive appraisal of new HCC systems that are equitable, trustworthy, and fairness-aware. For a broader impact, we aim to disseminate the outcomes and products from this topic to a wide range of communities, helping our peers as well as general, layman readers to understand the cutting-edge technologies of HCC. To this end, both theoretical and applied results with algorithms and applications are desired. This special issue offers a concentrative venue for researchers to make the rapid exchange of ideas and original research findings in Human-Centric Computing, Cognitive Algorithms, and robotic system application.
We encourage researchers and experts worldwide to contribute by submitting high-quality original research papers and systematic review papers. New interdisciplinary approaches, open-source tools, open-source datasets regarding Human-Centric Computing, Equitable AI and robotic systems are especially welcome. Areas to be covered in this Research Topic may include, but are not limited to:
• Explainable cognitive computing
• Brain-like computing
• Neuro-fuzzy computing
• Robust and safe computing
• Equitable AI-based human-machine systems
• Equitable AI-based human-robot interaction
• Equitable AI-based human-computer synergy/interaction
• AI Fairness & Ethnicity
• AI-based computing with social-justice prior
• Causal inference modeling
• Equitable AI-based new techniques and applications
• Robust AI-based robotic systems and applications

Keywords: Human-centric computing, Cognitive Computation, Artificial Intelligent, Trustworthy AI, Explainable machine learning, Privacy-preserved computing, Human Centered Robotics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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