About this Research Topic
Industry 5.0 places the worker at the center of the production process and uses new technologies to provide prosperity beyond jobs and growth while respecting the production limits of the planet. It complements the existing "Industry 4.0" approach by putting research and innovation at the service of the transition to a sustainable, human-centric, and resilient industry. AI has provided solutions that leverage data available from smart sensors, devices, and machines, it has enabled the generation of actionable intelligence and helped increase manufacturing efficiency. However, this evolution in AI has not been accompanied by similar emphasis and progress on fundamental aspects of human-centered processes and systems. Human-centered AI (HCAI) focuses on creating systems designed and developed by augmenting human intelligence with machine intelligence. HCAI is called to support the migration to Industry 5.0 as humans will have to collaborate with digital solutions such as AI systems, robots, and so forth.
The focus of this Research Topic is on advancements in Human-Centered Artificial Intelligence (HCAI) systems for Industry 5.0. Such systems view AI as a critical component for augmenting human work and extending human capabilities within an industrial environment, thus they can be considered key enablers for the “Operator 5.0” concept.
The aim of this Research Topic is to explore methods, tools and cases in which AI systems and humans work as teams within industrial settings in order to solve problems together and achieve goals that were unreachable by either humans or machines alone.
We welcome submissions on the following topics but not limited to:
Design issues for multimodal human-AI interactions in the industrial environment
• Digital intelligent assistants, softbots and chatbots for production management
• Artificial Intelligence for the operator in the Industry 5.0 environment
• Cognitive computing and HCAI engineering
• Explainable and transparent AI in smart manufacturing
• Usability and user experience of HCAI systems
• Methods, and tools to manage and monitor industrial HCAI systems
• Applications of HCAI systems in smart manufacturing
• Human-centered security and privacy issues in AI deployments
• Evaluation and performance metrics of HCAI case studies
• Cases and lessons learned from HCAI industrial experiments or large-scale rollouts
The focus of this Research Topic is on advancements in Human-Centered Artificial Intelligence (HCAI) systems for Industry 5.0. Such systems view AI as a critical component for augmenting human work and extending human capabilities within an industrial environment, thus they can be considered key enablers for the “Operator 5.0” concept.
The aim of this Research Topic is to explore methods, tools and cases in which AI systems and humans work as teams within industrial settings in order to solve problems together and achieve goals that were unreachable by either humans or machines alone.
We welcome submissions on the following topics but not limited to:
Design issues for multimodal human-AI interactions in the industrial environment
• Digital intelligent assistants, softbots and chatbots for production management
• Artificial Intelligence for the operator in the Industry 5.0 environment
• Cognitive computing and HCAI engineering
• Explainable and transparent AI in smart manufacturing
• Usability and user experience of HCAI systems
• Methods, and tools to manage and monitor industrial HCAI systems
• Applications of HCAI systems in smart manufacturing
• Human-centered security and privacy issues in AI deployments
• Evaluation and performance metrics of HCAI case studies
• Cases and lessons learned from HCAI industrial experiments or large-scale rollouts
Keywords: Human-Centered AI, Industry 5.0, Manufacturing, Industry
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.
