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Empowering Interactions: Advancing Human-Centred AI for Transparent, Collaborative and Accessible Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 1479

Special Issue Editors


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Guest Editor
School of Physical Sciences and Computing, University of Central Lancashire, Preston PR1 2HE, UK
Interests: human-centred artificial intelligence (HCAI); immersive learning in VR/AR/XR; serious games/gamification; web/mobile applications (including smart city games); cognitive HCI/socio-cognitive HCI; QoE research; multimodal biometric research

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Guest Editor
School of Computer Science and Engineering, University of Westminster, London W1B 2HW, UK
Interests: cultural informatics; human computer interaction; experimental usability; 3D modeling and animation; computer science; social sciences; engineering; decision sciences; mathematics; medicine energy business; management and accounting biochemistry; genetics and molecular biology

Special Issue Information

Dear Colleagues,

For the Special Issue "Empowering Interactions: Advancing Human-Centred AI for Transparent, Collaborative and Accessible Applications", we are seeking submissions of cutting-edge research that pushes the boundaries of AI technology to be inclusive, dependable, and seamlessly integrated across diverse sectors. This Special Issue aims to explore the enhancement of human–AI interactions, advance user accessibility, and promote trust and transparency within AI applications, emphasising the critical role of user-centric design. The scope of this issue includes a broad range of themes, including the following:

  1. Designing Transparent AI Interfaces: To explore this theme, we are seeking research on creating AI interfaces that prioritise transparency, fairness, accountability, and privacy. The focus is on technical design principles and algorithms that ensure clarity in AI decision making and user control.
  2. Enhancing Human–AI interactions: We are seeking contributions related to AI systems that demonstrate effective real-world collaboration with humans. This theme emphasises advancements in natural language processing, cognitive AI architectures, and emotional AI to improve AI's adaptability in multiple sectors. Furthermore, we welcome submissions exploring frameworks and methodologies for understanding, interpreting, and assessing human–AI collaboration dynamics, underscoring how these insights support the development and refinement of collaborative AI systems.
  3. Designing AI experiences: This theme calls for research on developing and evaluating AI with a deep understanding of user needs, aiming for accessible, intuitive, and satisfying AI experiences informed by user studies. This includes biometric studies involving eye tracking and emotion recognition to measure users' physiological responses to AI interactions.
  4. AI-Driven Innovations in Public Health: We are also seeking submissions that highlight the application of AI in public health challenges, focusing on areas such as disease surveillance, health data analytics, and patient care optimisation. This theme emphasises the use of AI to enhance diagnostic accuracy, improve treatment efficacy, and streamline healthcare operations.
  5. Cognitive Modelling and Augmentation: We invite the submission of research on AI systems that model and augment human cognitive processes, including support for decision making, enhanced learning experiences, and improved cognitive productivity.
  6. Building Trustworthy AI Interfaces: For this theme, we are seeking contributions on developing AI interfaces that foster user trust through reliability and security. This includes studies on user perception analysis, secure interface designs, and methodologies for evaluating trust in AI systems. 
  7. Accessible Designs in AI: We seek research that improves the accessibility of AI interfaces by developing innovative designs that accommodate users with disabilities and diverse user backgrounds, showcasing approaches to universal design principles in AI applications.
  8. User Interface Design in AI for Governance—Optimising Interactions and Decision Making: This theme emphasises the importance of user interface design in integrating AI technologies within governance frameworks to improve decision-making processes and administrative efficiency. It invites the submission of research on crafting AI systems with intuitive and user-friendly interfaces that support streamlined administrative tasks, effective policy implementation, and transparent insights into governance decisions. The focus is on leveraging design principles to ensure AI tools in governance are accessible and facilitate easy, clear interactions for both officials and the public, enhancing AI's overall usability and effectiveness in governance contexts.
  9. Developing Intuitive AI Interfaces for Children: This research theme focuses on developing and evaluating user interfaces within AI systems tailored for Child–Computer Interaction (CCI). It emphasises creating intuitive, engaging, and safe AI interfaces specifically designed to meet children's developmental and cognitive needs. Studies should investigate the optimisation of UI elements to facilitate ease of use and interactive play, prioritising design principles that ensure child safety and privacy. The aim is to adapt AI technologies to support developmental milestones through storytelling, interactive play, and creative expression, assessing their impact on young users' cognitive and emotional well-being while maintaining a secure digital environment.

Dr. Ioannis Doumanis
Dr. Daphne Economou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI transparency
  • human–AI collaboration
  • user experience optimization
  • public health AI applications
  • cognitive AI modelling
  • trust and security in AI
  • AI accessibility
  • AI in governance
  • child-friendly interface design
  • biometric AI analysis

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Published Papers (1 paper)

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Research

15 pages, 3035 KiB  
Article
Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation
by Yong-gyom Kim, SeMo Yang and KangYoon Lee
Appl. Sci. 2024, 14(16), 6876; https://doi.org/10.3390/app14166876 - 6 Aug 2024
Viewed by 559
Abstract
In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive [...] Read more.
In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization. Full article
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