Human–Computer Interaction Models and Experiences for Internet of Things Systems and Edge Computing

Dear Colleagues,

At present, complex IoT systems encompass intelligent devices that produce a large amount of data. These systems can be studied from different perspectives and at any level of the typical multilayer stack used to manage them. These systems are increasingly autonomous and able to make decisions. The more systems become autonomous, the more it becomes relevant to have software and models, but also report on case studies and best practices. This enables us to understand and manage the human factor and the implications that the increased system autonomy has on people.

This Special Issue looks at this human factor in the processes across the entire range, from data production to the business models used to monetize the created value. In this context, human–computer interaction therefore has a broad meaning that includes all the different aspects of having humans in the loop, somehow involved in the processes executed over and by the complex systems.

In this respect, we welcome contributions that aim to share technologies, policies, or heuristics from either a theoretical or practical point of view. We also welcome case studies for any relevant vertical domain. Cases from Health, Factory of Future, Media, and Fintech are particularly welcome.

Potential topics include, but are not limited to:

HCI Trust and Acceptability

• Reducing information asymmetry to increase human trust in AI-based systems;
• Models and experiences of risk analysis in HCI systems to increase human trust;
• Models and experiences of users’ understanding and acceptability of complex and AI-based ICT systems;
• Integration of explainable AI into established workflows and industrial processes.

HCI Education/learning

• Innovative instruments, processes, and experiences to educate workers of the immersive use of AI-based complex systems;
• Innovative instruments, processes, and experiences to educate the general public of everyday life in a pervasive AI populated world;
• AI-based learning instruments to explain and increase the acceptability of complex AI-based systems.

HCI Cross-field support.

• Novel experiences, processes, and models to bridge the gap between social science and ICT for the complete assessment of HCI systems;
• Instruments and experience to support data scientists in the design and configuration of AI-based decision support systems.

HCI Practical Experiences and Business Cases.

• Impact on business models of users in the multiple capacities of data producers, data processors, and data consumers in complex AI-based systems;
• Industrial cases showing innovative aspects in HCI;
• Insights and findings resulting from survey analyses of HCI experiences.

Dr. Francesco Lelli
Dr. Stefano Modafferi
Guest Editors

Manuscript Submission Information

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