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Record Nr. |
UNISA996475771603316 |
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Titolo |
Artificial intelligence in HCI : 3rd international conference, AI-HCI 2022, held as part of the 24th HCI international conference, HCII 2022, virtual event, June 26 - July 1, 2022, proceedings / / edited by Helmut Degen and Stavroula Ntoa |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (623 pages) |
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Collana |
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Lecture Notes in Computer Science Ser. ; ; v.13336 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Human-computer interaction |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Foreword -- HCI International 2022 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 3rd International Conference on Artificial Intelligence in HCI (AI-HCI 2022) -- HCI International 2023 -- Contents -- Human-Centered AI -- Exploring the Design Context of AI-Powered Services: A Qualitative Investigation of Designers' Experiences with Machine Learning -- 1 Introduction -- 2 Literature Review -- 2.1 UX Design Practice and the Concept of a Design Material -- 2.2 Machine Learning as a Design Material -- 3 Method -- 3.1 Empirical Data Collection -- 3.2 Thematic Analysis -- 4 Findings -- 4.1 Absence of Competence -- 4.2 Lack of Incentive for Competence Development -- 4.3 Challenges Articulating Design Criteria -- 4.4 Mature Versus Immature Clients -- 4.5 Lack of Support for Ethical Concerns -- 4.6 Concluding Remarks -- 5 Discussion -- 6 Conclusion -- References -- Measuring and Predicting Human Trust in Recommendations from an AI Teammate -- 1 Trust in AI Teammates -- 2 Experimental Paradigm -- 3 Measures -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Promoting Human Competences by Appropriate Modes of Interaction for Human-Centered-AI -- 1 Introduction -- 2 Methodological Approach -- 3 Interaction Modes -- |
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3.1 Offering Explanations and Possibilities for Exploration -- 3.2 Testing -- 3.3 Initiating and Performing Re-training -- 3.4 Variation of Underlying Data Sets and Methods -- 3.5 Flexible Sequencing or Filtering of Data Input -- 3.6 Identification and Comparison of Similar Cases -- 3.7 Refinement -- 3.8 Intervention -- 3.9 Vetoing -- 3.10 Critiquing -- 4 Conclusion and Outlook -- References -- Artificial Intelligence Augmenting Human Teams. A Systematic Literature Review on the Opportunities and Concerns -- 1 Introduction -- 2 Methods. |
2.1 Planning the Literature Review -- 3 Findings -- 4 Discussion and Recommendation -- 5 Implications for Research and Practice -- 6 Conclusion and Limitations -- References -- Adoption and Perception of Artificial Intelligence Technologies by Children and Teens in Education -- 1 Introduction -- 2 Previous Work -- 3 Methodology -- 3.1 Keyword Search -- 3.2 Screening the Titles and Abstracts -- 3.3 Screening the Full Text -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Analysis of the Impact of Applying UX Guidelines to Reduce Noise and Focus Attention -- 1 Introduction -- 2 Proposal and Methodology -- 3 Results and Evaluation -- 4 Conclusions -- References -- Gamifying the Human-in-the-Loop: Toward Increased Motivation for Training AI in Customer Service -- 1 Introduction -- 2 Related Work -- 3 Research Approach -- 4 Objectives of a Solution -- 5 Artifact Design and Development -- 6 Demonstration -- 7 Evaluation -- 8 Discussion and Conclusion -- References -- Explainable and Trustworthy AI -- Dominant View and Perception of Artificial Intelligence in Developing Economy -- 1 Introduction -- 2 Method -- 2.1 Participants -- 2.2 Instruments -- 3 Results -- 3.1 AI Powers African Economic Development -- 3.2 AI Systems Create More Jobs Than They Eliminate -- 3.3 Support for AI System Development -- 3.4 AI Induce Algorithmic Colonialism -- 3.5 Openness of AI Operation -- 3.6 African Readiness to Adopt AI -- 4 Discussion -- 5 Conclusion -- References -- MoReXAI - A Model to Reason About the eXplanation Design in AI Systems -- 1 Introduction -- 2 Foundations and Related Works -- 2.1 Ethical Principles and Explainable Artificial Intelligence (XAI) -- 2.2 Semiotic Engineering -- 2.3 Related Works -- 3 The Model for Reasoning About AI Explanation Design -- 3.1 Model Questions -- 3.2 MoReXAI Structure -- 3.3 MoReXai Use -- 4 Case Study. |
4.1 Case Study Planning -- 4.2 Model Application -- 4.3 Results: Talking About Explanations -- 5 Discussion -- 5.1 About the Epistemic Character of the MoReXAI -- 5.2 Improvements to the MoReXAI -- 6 Conclusão -- References -- (De)Coding Social Practice in the Field of XAI: Towards a Co-constructive Framework of Explanations and Understanding Between Lay Users and Algorithmic Systems -- 1 Introduction -- 2 Method -- 3 Discussion/Empirical Insights -- 4 Conclusion -- References -- Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Suicide Dataset Collection -- 3.2 Feature Extraction -- 3.3 Feature Selection -- 3.4 Statistical Analysis -- 3.5 Classification -- 4 Results and Discussion -- 4.1 Interpretation of Nonverbal Behavior -- 4.2 Classification Results -- 4.3 Limitations -- 5 Conclusion -- References -- ExMo: Explainable AI Model Using Inverse Frequency Decision Rules -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Review of the Bayesian Rule List (BRL) Algorithm -- 3.2 Decision Rules from TF-IDF -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Classification Accuracy -- 4.3 Model Comparison -- 4.4 Textual Explanation -- 4.5 Comparison with Model-Agnostic Explanation -- 4.6 Execution Time -- 5 Conclusion -- References -- UX Design and Evaluation of AI-Enabled Systems -- A |
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Model of Adaptive Gamification in Collaborative Location-Based Collecting Systems -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Space-Time Behaviour Definitions -- 3.2 Game Challenge Definitions -- 3.3 System Setup Definitions -- 4 CLCS Automatic Game Challenge Recommendation -- 4.1 Challenge Repository Population -- 4.2 Challenge Difficulty Estimation -- 4.3 Challenge Reward Computation -- 4.4 Challenge Sorting -- 5 Discussion -- 6 Conclusions and Future Work. |
References -- Benchmarking Neural Networks-Based Approaches for Predicting Visual Perception of User Interfaces -- 1 Introduction -- 2 Methods and Related Work -- 2.1 Neural Networks in Computer Vision -- 2.2 UI Feature Extraction -- 3 The Experimental Study Description -- 3.1 Hypotheses and Design -- 3.2 Material and the Input Data -- 3.3 Subjects and the Output Data -- 3.4 The Models: ANN and CNN -- 4 Results -- 4.1 Descriptive Statistics -- 4.2 The Models' Training Time -- 4.3 Benchmarking the Models' MSEs -- 4.4 Regression Analysis for MSE -- 5 Discussion and Conclusion -- References -- My Tutor is an AI: The Effects of Involvement and Tutor Type on Perceived Quality, Perceived Credibility, and Use Intention -- 1 Introduction -- 2 Related Work -- 2.1 AI Tutors -- 2.2 The Elaboration Likelihood Model (ELM) and Computers are Social Actors (CASA) -- 2.3 Attribution Theory -- 3 Method -- 3.1 Participants -- 3.2 Procedure -- 3.3 Stimuli -- 3.4 Measurement -- 4 Results -- 5 Discussion -- 5.1 Contributions and Implications -- 5.2 Limitations -- 6 Conclusion -- References -- Design of AI-Enabled Application to Detect Ayurvedic Nutritional Values of Edible Items and Suggest a Diet -- 1 Introduction -- 2 Literature Review and Related Works -- 2.1 Understanding Ayurveda -- 2.2 Understanding Ayurvedic Diet System -- 2.3 Understanding Quantified Self -- 2.4 Understanding Major Determinants of Food Choice -- 3 Research Approach -- 4 Interview Study -- 4.1 Study Setup and Method -- 4.2 Results of Initial Interview Study -- 5 Design and Implementation -- 5.1 Determination of Nutritional Values and Ayurvedic Gunas for Food Items -- 5.2 Diet Suggestions Based on Body Type -- 6 Usability Testing -- 6.1 Task 1: Determine Your Body Type in the Application Using Quiz -- 6.2 Task 2: Scan Any Food Item and Determine Its Ayurvedic Nutritional Information. |
6.3 Task 3: Track Your Diet and Mood in the Application -- 6.4 Gamification -- 7 Conclusion -- References -- An AI-Based Decision Support System for Quality Control Applied to the Use Case Donor Cornea -- 1 Introduction -- 2 Related Work -- 3 Decision Support System -- 3.1 Data -- 3.2 Graphical Analytic Tools -- 3.3 Case-Based Reasoning as a Tool for Explainability -- 3.4 Machine Learning Classifiers -- 3.5 Aggregation -- 3.6 Graphical User Interface -- 4 Evaluation -- 4.1 Study Set-Up -- 4.2 Study Execution -- 4.3 Study Results -- 5 Summary and Future Work -- References -- Design and Implementation of Platform Protocol and Client of Hakka Residential System Based on Artificial Intelligence -- 1 Introduction -- 2 Method -- 2.1 Application Fields of Artificial Intelligence -- 2.2 Hakka Houses -- 2.3 Smart Home System -- 2.4 Function of Smart Home System -- 2.5 Calculation Methods Used in the Experiment -- 3 Experiment -- 3.1 Selection of Experimental Research Objects -- 3.2 Selection of Experimental Measurement Standards -- 4 Discussion -- 4.1 Investigation on Design Efficiency of the Two Companies -- 4.2 Client Utilization Survey Designed by Two Companies -- 5 Conclusions -- References -- Framework for User Experience Evaluation in MOOC Platforms -- 1 Introduction -- 2 Introduction -- 2.1 Massive and Open Online Courses -- 2.2 Classification of MOOC Criteria -- 2.3 User |
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Experience -- 2.4 Categories and Factors that Make Up the UX -- 2.5 UX Dimensions -- 3 Proposal -- 3.1 FUXE-MOOC Design -- 3.2 FUXE-MOOC Components -- 4 Limitations and Future Research -- 5 Conclusions -- Appendix -- References -- Evaluation on Comfortable Arousal in Autonomous Driving Using Physiological Indexes -- 1 Introduction -- 2 Research Objective -- 3 Research Method -- 3.1 Physiological Indexes Based on Feature Importance -- 3.2 Data Set and Model Creation -- 4 Experiment. |
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