Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part II

• Autore: Degen Helmut
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (480 pages)
• Altri autori: NtoaStavroula
• Nota di contenuto: Intro -- Foreword -- HCI International 2024 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 5th International Conference on Artificial Intelligence in HCI (AI-HCI 2024) -- HCI International 2025 Conference -- Contents - Part II -- Ethical Considerations and Trust in AI -- Effects of Explanations by Robots on Trust Repair in Human-Robot Collaborations -- 1 Introduction -- 2 Method -- 2.1 Participants -- 2.2 Design and Procedure -- 2.3 Measures and Analyses -- 3 Results -- 3.1 Perceptions of Trustworthiness -- 3.2 Effects of Explanations on Trust Repair -- 4 Discussion -- 4.1 Limitations and Future Work -- 5 Conclusion and Implications -- References -- Do You Trust AI? Examining AI Trustworthiness Perceptions Among the General Public -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Results -- 4.1 The Most and Least Important Aspects that Influence Trust in AI -- 4.2 Changes in Trust at the Beginning and End of the Questionnaire -- 4.3 Results from Analysis of the Open-Ended Question -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Factors of Trust Building in Conversational AI Systems: A Literature Review -- 1 Introduction -- 2 Background -- 2.1 Trust -- 2.2 Artificial Intelligence -- 2.3 Conversational AI Systems -- 3 Related Work -- 3.1 Concept of Trust -- 3.2 Key Challenges in Trusting an AI System -- 3.3 Factors Affecting Trust in Human-Computer Interaction -- 4 Methodology -- 5 Results -- 5.1 Trust-Building Factors in Conversational AI-Based Systems -- 5.2 How to Build and Improve Trust -- 5.3 Best Practices of Conversational AI Systems -- 6 Discussion -- 7 Conclusion -- 8 Limitations and Future Research -- References -- BlocklyBias: A Visual Programming Language for Bias Identification in AI Data -- 1 Introduction -- 2 Related Works. ; 2.1 Visual Languages in Data Analysis -- 3 BlocklyBias -- 3.1 Design -- 3.2 Development -- 3.3 Testing -- 4 Evaluation -- 4.1 Goals -- 4.2 Research Question -- 4.3 Study Design -- 4.4 Participants -- 4.5 Settings and Tasks -- 4.6 Procedure -- 4.7 Findings -- 5 Discussion -- 6 Conclusions -- References -- Comparing Socio-technical Design Principles with Guidelines for Human-Centered AI -- 1 Introduction -- 2 Background -- 3 Method -- 4 Findings -- 5 AI-Related Revision of Sociotechnical Heuristics -- 6 Discussion and Conclusion -- References -- Negative Emotions Towards Artificial Intelligence in the Workplace - Motivation and Method for Designing Demonstrators -- 1 Introduction -- 2 Negative Emotions Related to AI in the Workplace -- 3 Empirical Survey: Employee Reactions to AI in the Workplace in Manufacturing Industry -- 3.1 Research Design -- 3.2 Results of the Survey -- 3.3 Conclusions from the Survey -- 4 Approaches to Create Acceptance and Trust in AI in the Workplace -- 5 Method for Designing a Demonstrator to Reduce Negative Emotions About AI in the Workplace -- 6 Summary and Outlook -- References -- Why Designers Must Contribute to Responsible AI -- 1 The Need for Responsible AI -- 2 The Impact of the Interactional Approach -- 3 Case Studies -- 3.1 Design -- 3.2 Data Collection and Participants -- 3.3 Validating the Online Survey Design -- 4 Mental Models and Control -- 4.1 Case 1 Digital Learning: Mental Models -- 4.2 Case 2 Streaming Video: Design Affecting Control -- 5 Design Choices Affecting Trust -- 5.1 Case 3: Smart Sustainability Websites -- 5.2 Case 4 Professional Social Media -- 6 Discussion -- References -- Learning Fair Representations: Mitigating Statistical Dependencies -- 1 Introduction -- 2 Preliminaries -- 2.1 Fairness Metric -- 2.2 Fair Representation Learning -- 3 Methodology -- 4 Experiments -- 4.1 Dataset -- 4.2 Setting. ; 5 Results and Discussion -- 6 Conclusion -- References -- Model-Free Motion Planning of Complex Tasks Subject to Ethical Constraints -- 1 Introduction -- 2 Preliminary -- 2.1 Parially Observable Markov Decision Process (POMDP) -- 2.2 Linear Temporal Logic (LTL) -- 2.3 Limit-Deterministic Generalized Büchi Automaton (LDGBA) -- 3 Methodologies -- 3.1 Ethical Constraints -- 3.2 Product POMDP -- 3.3 Problem Definition -- 3.4 Q-Learning -- 4 Example -- 4.1 Scenario 1: Deontological Government and Company but Utilitarian Protesters -- 4.2 Scenario 2: Utilitarian Government and Company but Deontological Protesters -- 4.3 Scenario 3: Utilitarian Government, Company, and Protesters -- 5 Conclusion -- References -- Enhancing User Experience Through AI-Driven Technologies -- Incorporating Artificial Intelligence into Design Criteria Considerations -- 1 Introduction and Background -- 1.1 Improving User Interaction by Understanding User Attention -- 1.2 The Interdisciplinary Approach to the Design of Effective UIs -- 1.3 Assessing AI Integration into Design Criteria for UI Design -- 2 Method -- 2.1 Identifying Underlying Conditions -- 2.2 Defining Scope and Boundaries -- 2.3 Validation Process -- 3 Identifying Underlying Conditions -- 4 Scope and Boundaries -- 5 Literature Review (Review Process) -- 5.1 Conducting the Literature Review -- 5.2 Summary of the Results -- 6 Comparison (Analysis Process) -- 6.1 Pre-Considerations Incorporating AI into Design Criteria -- 6.2 Design Criteria to Consider When Integrating AI into UI Design -- 7 Conclusion and Outlook -- 7.1 Outlook -- 7.2 Limitations -- References -- Multimodal Interfaces for Emotion Recognition: Models, Challenges and Opportunities -- 1 Introduction -- 2 Modalities for Emotion Recognition -- 3 Multimodal Fusion -- 4 Applications of Multimodal Emotion Recognition -- 5 Open Challenges and Opportunities. ; 6 Conclusion -- References -- Co-creation with AI in Car Design: A Diffusion Model Approach -- 1 Introduction -- 2 Related Work -- 2.1 Car Design Semantic Corpus -- 2.2 Text-to-Image Diffusion Model -- 2.3 ControlNet -- 2.4 DreamBooth Training Approach -- 3 Method -- 3.1 Car Design Data Set -- 3.2 Workflow Framework -- 3.3 Training -- 3.4 Model Testing -- 4 Result -- 4.1 Generation Rounds and Time -- 4.2 Comparison -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- A Study on the Application of Generative AI Tools in Assisting the User Experience Design Process -- 1 Introduction -- 2 The Impact of AI on UX Design -- 3 Method -- 4 Results -- 4.1 Problem Distillation Stage -- 4.2 Prototype Design Stage -- 4.3 Design Validation Stage -- 4.4 Summary of Research Findings -- 5 Discussion -- 5.1 The Benefits and Limitations of Generative AI Assistance in the UX Design Process -- 5.2 Potential Applications of Generative AI Across Different Design Fields -- 5.3 How Generative AI May Reshape the Future of UX Designers -- 5.4 Research Limitations and Future Research Directions -- 6 Conclusions and Future Works -- 6.1 Generative AI Efficiently Streamlines Early-Stage Design Processes, Enabling a Greater Focus on Creativity and Strategy -- 6.2 Generative AI Should Offer Clear Decision-Making Processes to Facilitate Precise Questioning by Designers -- References -- Estimating Reliability of Speech Recognition Results Using Recognition Results Change Degree When Minute Changes to Speech Waveform -- 1 Introduction -- 2 Analysis of Speech Recognition Errors in Daily Conversation -- 2.1 Speech Recognition System -- 2.2 Conversation Database -- 2.3 Speech Recognition Results -- 3 The Reliability Estimation Method of Speech Recognition Result by Processing Slight Changes to Input Speech Data. ; 4 Relationship Between Speech Recognition Performance and Recognition Result Change Rate -- 5 Evaluation of Reliability Estimation Performance of Speech Recognition Results -- 6 Applications Examples Using the Recognition Result Reliability Estimation Method -- 7 Conclusion -- References -- Emotion Detection from Facial Expression in Online Learning Through Using Synthetic Image Generation -- 1 Introduction -- 2 Related Work -- 3 Framework for Educational Emotion Detection -- 3.1 Synthetic Image Generation -- 3.2 A Lightweight Model for Educational Emotion Detection -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Experiment Setup -- 4.3 Results and Discussion -- 4.4 Statistical Analysis -- 5 Conclusion and Future Work -- References -- Design Method of AI Composing Smart Speaker Based on Humming Behavior -- 1 Introduction -- 2 Related Concept -- 3 Method and Design Process -- 3.1 Behavior-Function Intersection Evaluation Model -- 3.2 Questionnaire Design -- 3.3 Questionnaire Reliability and Validity Test -- 4 AI Composing Smart Speaker Based on Humming Behavior -- 4.1 Design Style Orientation -- 4.2 AI Composition -- 4.3 Human-Computer Interaction Process -- 4.4 Product Appearance Design -- 4.5 Idea Sketch -- 4.6 3D Modeling -- 4.7 Product Introduction -- 4.8 App UI Design -- 4.9 Product Scene Renderings -- 5 Conclusion -- References -- Leveraging Generative AI Concepts with Uncertainty of Information Concept -- 1 Introduction -- 1.1 Generative AI, Large Language Models, Foundation Models -- 1.2 Type of Models -- 1.3 Data -- 2 Uncertainty of Information Concept -- 2.1 A Subsection Sample -- 3 Generative Uncertainty of Information -- 3.1 First GUoI Idea -- 3.2 Second GUoI Idea -- 3.3 Third GUoI Idea -- 3.4 Fourth GUoI Idea -- 3.5 Fifth GUoI Idea -- 3.6 Sixth GUoI Idea -- 4 Conclusion -- References. ; Cybersickness Detection Through Head Movement Patterns: A Promising Approach.
• Titolo autorizzato: Artificial Intelligence in HCI
• ISBN: 9783031606113; 9783031606137
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910865281303321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science Series