The AI in HCI Conference aims to bring together academics, practitioners and students to exchange results from academic and industrial research, as well as industrial experiences, on the use of Artificial Intelligence technologies to enhance Human-Computer Interaction. In particular, the following areas of research are relevant: (i) Ethical and trustworthy AI to provide a fair and unbiased experience; (ii) Evolution of Human-Centered AI including models, processes and modalities; (iii) Generative AI tools, methods, and processes; (iv) Human-AI Interaction and collaboration; (v) Human-centered AI/ML technologies; (vi) Use of AI to support basic human needs; and (vii) AI in HCI for consumer and industrial application domains.
The Conference is targeted at individuals and organizations who have performed research or developed applications in the area of AI in HCI. The Conference is also targeted at individuals and organizations which want to learn from those results, so they can (re-)use them in research or industrial applications.
Call for participation leaflet (52KB)
(Please create an account to the Conference Management System before you proceed with the submission)
The AI in HCI Conference welcomes work with a strong user, human, or society focus.
The related topics include, but are not limited to:
- Ethical and trustworthy AI
- Identifying and addressing biases and potential conflicts such as fairness, privacy, equity, diversity, sustainability, power assignment and distribution, norms, values / beliefs
- Explainable AI, transparency, reliability, trust, and fairness
- Metrics and Key Performance Indicators (KPIs)
- Human-Centered AI
- Models: human modeling, social models, dialog / interaction models, technology models
- Processes, tools, methods, standards, multi-disciplinary collaboration
- Design thinking: methods, processes, tools, and case studies
- Data management: data selection, data generation, data quality, data annotation, training data, testing data
- Prototyping / simulation
- Personalizable and adaptable User Interfaces, Intelligent User Interfaces, affective User Interfaces
- User involvement, user research, evaluation, AI technology assessment and customization
- Generative AI
- AI-based content generation, such as text, images, videos, 3D models, etc.
- control of outcomes, prompt engineering, bias, hallucinations, etc.
- Generation of process artifacts, such as definition of user goals, user models, personas / user roles, usability test results, storytelling and narratives, etc.
- Generative UI/UX: UI design, personalized UIs, collaborative creativity
- Human-AI Interaction
- Conversational modalities, such as chatbots and intelligent personal assistants
- Human-robot teaming and interactions
- Human-agent collaboration and interactions
- Interaction paradigms: gesture-based interactions, implicit interactions, virtual and augmented reality, speech-based interaction, brain-computer interfaces, natural language interaction
- Human-centered AI/ML technologies
- Visualizations of ML model results and properties: explaining decision-making processes of ML models, feature selection and extraction techniques, transparent and interpretable visualization of ensemble methods, model’s uncertainty and risk assessment
- Visual interactive AI/ML model discovery
- Lossless visualization on AI/ML high-dimensional data
- Interactive ML algorithms for high stakes AI/ML tasks with human-in-the-loop
- Methods to counter quasi-explanations of AI/ML models
- Investigation of the trade-offs between model complexity and interpretability
- Robust, safe, and secure ML technologies
- ML life cycle / ML operations (MLOps)
- Use of AI to support fundamental human needs, such as
- Fair supply of and access to healthy and affordable food and water
- Fair supply of and access to affordable education and personal growth
- Fair supply of and access to affordable healthcare
- Sustainable use of resources
- Being treated respectfully and fairly without bias and discrimination
- Increasing inclusion and reducing inequalities
- Fostering environmental sustainability, responsible consumption, and production
- Consumer and industrial application domains
- Healthcare and well-being: diagnostics support, treatment suggestions including explainability, evidence and confidence, e-health, personalized healthcare, e-IoT, social assistive robots
- Cultural and art applications: writing, painting, drawing, composing, etc.,
- Financial applications: trends, predictions, bids, risk assessments, recommendations
- Market places: match finding, trending, bidding, offering
- Manufacturing and robots: human-robot teaming, human-robot interaction, safety, factory automation and optimization, digital twins, simulations, etc.
- (Semi-) Autonomous transportation: monitoring and control, explainability, evidence and confidence, ethical conflict resolution, safety, social navigation
- Smart dashboards: status, deviations, recommendations for preventive and corrective actions including explainability, evidence and confidence
- Personalized education and e-learning: assessment, planning, content selection, progress measurements
- Security: predicting and identifying vulnerabilities, predicting and suggesting mitigations, selecting and executing mitigations, monitoring incidents, penetration testing, digital forensics
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Program Chair
Helmut Degen
Siemens Corporation, United States
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Program Chair
Stavroula Ntoa
Foundation for Research & Technology - Hellas (FORTH), Greece
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Board Members
- Silvio Barra
Università degli Studi di Napoli, Italy - Federico Cabitza
Università degli Studi di Milano-Bicocca, Italy - Luis Castro
Sonora Institute of Technology (ITSON), Mexico - Weiqin Chen
Oslo Metropolitan University, Norway - Gennaro Costagliola
Università di Salerno, Italy - Ahmad Esmaeili
Wichita State University, United States - Ozlem Ozmen Garibay
University of Central Florida, United States - Julian Grigera
LIFIA, Fac. de Informática - UNLP, Argentina - Pei-Hsuan Hsieh
National Chengchi University, Taiwan - Boris Kovalerchuk
Central Washington University, United States - Sandeep Kaur Kuttal
North Carolina State University, United States - Carsten Lanquillon
Hochschule Heilbronn, Germany - Mahnaz Moallem
Towson University, United States - Jennifer Moosbrugger
Siemens, Germany - Ming Qian
Charles River Analytics, United States - Adrienne Raglin
Army Research Lab, United States - Robert Reynolds
Wayne State University, United States - Marco Romano
Università degli Studi Internazionali di Roma - UNINT, Italy - Gabriele Trovato
Shibaura Institute of Technology, Japan - Giuliana Vitiello
University of Salerno, Italy - Carsten Wittenberg
Hochschule Heilbronn/Heilbronn University oAS, Germany
Disclaimer - Political Neutrality
The HCI International Conference respects the decisions of all its contributors, engaged in any way, regarding their institutional affiliations and designations of territories, in all material / content published in its website, taking a neutral stance in relation to any disputes or claims. Moreover, the HCI International Conference fully concurs with the Territorial Neutrality Policy of Springer Nature, Publisher of its proceedings.