Relevant bibliographies by topics / Human-Centered Artificial Intelligence

Academic literature on the topic 'Human-Centered Artificial Intelligence'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Human-Centered Artificial Intelligence"

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Cheruvu, Ria. "Unconventional Concerns for Human-Centered Artificial Intelligence." Computer 55, no. 7 (July 2022): 46–55. http://dx.doi.org/10.1109/mc.2022.3170423.

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Riedl, Mark O. "Human‐centered artificial intelligence and machine learning." Human Behavior and Emerging Technologies 1, no. 1 (January 2019): 33–36. http://dx.doi.org/10.1002/hbe2.117.

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Ozmen Garibay, Ozlem, Brent Winslow, Salvatore Andolina, Margherita Antona, Anja Bodenschatz, Constantinos Coursaris, Gregory Falco, et al. "Six Human-Centered Artificial Intelligence Grand Challenges." International Journal of Human–Computer Interaction 39, no. 3 (January 2, 2023): 391–437. http://dx.doi.org/10.1080/10447318.2022.2153320.

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Shneiderman, Ben. "Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy." International Journal of Human–Computer Interaction 36, no. 6 (March 23, 2020): 495–504. http://dx.doi.org/10.1080/10447318.2020.1741118.

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Pisoni, Galena, Natalia Díaz-Rodríguez, Hannie Gijlers, and Linda Tonolli. "Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage." Applied Sciences 11, no. 2 (January 19, 2021): 870. http://dx.doi.org/10.3390/app11020870.

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This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.
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Veitch, Erik, and Ole Andreas Alsos. "Human-Centered Explainable Artificial Intelligence for Marine Autonomous Surface Vehicles." Journal of Marine Science and Engineering 9, no. 11 (November 6, 2021): 1227. http://dx.doi.org/10.3390/jmse9111227.

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Explainable Artificial Intelligence (XAI) for Autonomous Surface Vehicles (ASVs) addresses developers’ needs for model interpretation, understandability, and trust. As ASVs approach wide-scale deployment, these needs are expanded to include end user interactions in real-world contexts. Despite recent successes of technology-centered XAI for enhancing the explainability of AI techniques to expert users, these approaches do not necessarily carry over to non-expert end users. Passengers, other vessels, and remote operators will have XAI needs distinct from those of expert users targeted in a traditional technology-centered approach. We formulate a concept called ‘human-centered XAI’ to address emerging end user interaction needs for ASVs. To structure the concept, we adopt a model-based reasoning method for concept formation consisting of three processes: analogy, visualization, and mental simulation, drawing from examples of recent ASV research at the Norwegian University of Science and Technology (NTNU). The examples show how current research activities point to novel ways of addressing XAI needs for distinct end user interactions and underpin the human-centered XAI approach. Findings show how representations of (1) usability, (2) trust, and (3) safety make up the main processes in human-centered XAI. The contribution is the formation of human-centered XAI to help advance the research community’s efforts to expand the agenda of interpretability, understandability, and trust to include end user ASV interactions.
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Ford, Kenneth M., Patrick J. Hayes, Clark Glymour, and James Allen. "Cognitive Orthoses: Toward Human-Centered AI." AI Magazine 36, no. 4 (December 31, 2015): 5–8. http://dx.doi.org/10.1609/aimag.v36i4.2629.

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This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.
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Mehta, Ranjana K., Jason Moats, Rohith Karthikeyan, Joseph L. Gabbard, Divya Srinivasan, Eric Jing Du, Alexander Leonessa, Garret Burks, Andrew Stephenson, and Ron Fernandes. "Human‐centered intelligent training for emergency responders." AI Magazine 43, no. 1 (March 2022): 83–92. http://dx.doi.org/10.1002/aaai.12041.

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Mehta, Ranjana, Jason Moats, Rohith Karthikeyan, Joseph Gabbard, Divya Srinivasan, Eric Du, Alexander Leonessa, Garret Burks, Andrew Stephenson, and Ron Fernandes. "Human-Centered Intelligent Training for Emergency Responders." AI Magazine 43, no. 1 (March 31, 2022): 83–92. http://dx.doi.org/10.1609/aimag.v43i1.19129.

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Emergency response (ER) workers perform extremely demanding physical and cognitive tasks that can result in serious injuries and loss of life. Human augmentation technologies have the potential to enhance physical and cognitive work-capacities, thereby dramatically transforming the landscape of ER work, reducing injury risk, improving ER, as well as helping attract and retain skilled ER workers. This opportunity has been significantly hindered by the lack of high-quality training for ER workers that effectively integrates innovative and intelligent augmentation solutions. Hence, new ER learning environments are needed that are adaptive, affordable, accessible, and continually available for reskilling the ER workforce as technological capabilities continue to improve. This article presents the research considerations in the design and integration of use-inspired exoskeletons and augmented reality technologies in ER processes and the identification of unique cognitive and motor learning needs of each of these technologies in context-independent and ER-relevant scenarios. We propose a human-centered artificial intelligence (AI) enabled training framework for these technologies in ER. Finally, how these human-centered training requirements for nascent technologies are integrated in an intelligent tutoring system that delivers across tiered access levels, covering the range of virtual, to mixed, to physical reality environments, is discussed.
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Correia, Joachim Hereth, Gerd Stumme, Rudolf Wille, and Uta Wille. "Conceptual knowledge discovery--a human-centered approach." Applied Artificial Intelligence 17, no. 3 (March 2003): 281–302. http://dx.doi.org/10.1080/713827122.

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Dissertations / Theses on the topic "Human-Centered Artificial Intelligence"

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Srivastava, Akshat. "Developing Functional Literacy of Machine Learning Among UX Design Students." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617104876484835.

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Çerçi, Sena. "Cyborg, How Queer Are You? Speculations on Technologically-Mediated Morality Towards Posthuman-Centered Design." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22476.

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This research deals with the highly-relevant issue of paternalism within the discipline and practice of HCI with a particular focus on the autonomous decision-making AI technologies. It is an attempt to reframe the problem of paternalism as a basis for posthuman-centered design, as the emerging technologies have already started to redefine autonomy, morality and therefore what it means to be a human. Instead of following traditional design processes, queering as an analogy/method is used in order to speculate on the notion of technological mediation through design fictions. Relying on arguments drawn from the relevant theory on philosophy of technology and feminist technoscience studies as well as the insights from the fieldwork rather than conventional empirical design research for its conclusions, this research aims to provide a background for a possible ‘Design Thing’ to tackle the problem in multidisciplinary and democratic ways under the guidance of the ‘queer cyborg’ imagery.
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Mitchell, Elliot Griffith. "Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence." Thesis, 2021. https://doi.org/10.7916/d8-drwc-b875.

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Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching.
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Vorm, Eric Stephen. "Into the Black Box: Designing for Transparency in Artificial Intelligence." Diss., 2019. http://hdl.handle.net/1805/21600.

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Indiana University-Purdue University Indianapolis (IUPUI)
The rapid infusion of artificial intelligence into everyday technologies means that consumers are likely to interact with intelligent systems that provide suggestions and recommendations on a daily basis in the very near future. While these technologies promise much, current issues in low transparency create high potential to confuse end-users, limiting the market viability of these technologies. While efforts are underway to make machine learning models more transparent, HCI currently lacks an understanding of how these model-generated explanations should best translate into the practicalities of system design. To address this gap, my research took a pragmatic approach to improving system transparency for end-users. Through a series of three studies, I investigated the need and value of transparency to end-users, and explored methods to improve system designs to accomplish greater transparency in intelligent systems offering recommendations. My research resulted in a summarized taxonomy that outlines a variety of motivations for why users ask questions of intelligent systems; useful for considering the type and category of information users might appreciate when interacting with AI-based recommendations. I also developed a categorization of explanation types, known as explanation vectors, that is organized into groups that correspond to user knowledge goals. Explanation vectors provide system designers options for delivering explanations of system processes beyond those of basic explainability. I developed a detailed user typology, which is a four-factor categorization of the predominant attitudes and opinion schemes of everyday users interacting with AI-based recommendations; useful to understand the range of user sentiment towards AI-based recommender features, and possibly useful for tailoring interface design by user type. Lastly, I developed and tested an evaluation method known as the System Transparency Evaluation Method (STEv), which allows for real-world systems and prototypes to be evaluated and improved through a low-cost query method. Results from this dissertation offer concrete direction to interaction designers as to how these results might manifest in the design of interfaces that are more transparent to end users. These studies provide a framework and methodology that is complementary to existing HCI evaluation methods, and lay the groundwork upon which other research into improving system transparency might build.
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Books on the topic "Human-Centered Artificial Intelligence"

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Khosla, Rajiv. Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach. Boston, MA: Springer US, 2000.

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Working with preferences: Less is more. Heidelberg: Springer, 2011.

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1954-, Forbrig Peter, Bernhaupt Regina, and SpringerLink (Online service), eds. Human-Centered Software Engineering: 4th International Conference, HCSE 2012, Toulouse, France, October 29-31, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Nev.) International Conference on Augmented Cognition (7th 2013 Las Vegas. Foundations of augmented cognition: 7th international conference, AC 2013, held as part of HCI International 2013, Las Vegas, NV, USA, July 21-26, 2013 : proceedings. Berlin: Springer, 2013.

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service), SpringerLink (Online, ed. Human Centered Design: First International Conference, HCD 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Cipolla-Ficarra, Francisco V. Advances in New Technologies, Interactive Interfaces and Communicability: Second International Conference, ADNTIIC 2011, Huerta Grande, Argentina, December 5-7, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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International Conference on User Modeling (11th 2007 Corfu, Greece). User modeling 2007: 11th international conference, UM 2007, Corfu, Greece, June 25-29, 2007 : proceedings. Berlin: Springer, 2007.

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Leitner, Gerhard. HCI in Work and Learning, Life and Leisure: 6th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering, USAB 2010, Klagenfurt, Austria, November 4-5, 2010. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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Esposito, Anna. Cognitive Behavioural Systems: COST 2102 International Training School, Dresden, Germany, February 21-26, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Schmorrow, Dylan D. Foundations of Augmented Cognition. Directing the Future of Adaptive Systems: 6th International Conference, FAC 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Book chapters on the topic "Human-Centered Artificial Intelligence"

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Onisawa, Takehisa. "Soft Computing in Human Centered Systems Thinking." In Modeling Decisions for Artificial Intelligence, 36–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_5.

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Herrmann, Thomas. "Promoting Human Competences by Appropriate Modes of Interaction for Human-Centered-AI." In Artificial Intelligence in HCI, 35–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05643-7_3.

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Dorais, Gregory, and David Kortenkamp. "Designing Human-Centered Autonomous Agents." In Advances in Artificial Intelligence. PRICAI 2000 Workshop Reader, 321–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45408-x_32.

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Sarabia, Carmen. "Nursing, Soft Skills and Artificial Intelligence." In Soft Skills for Human Centered Management and Global Sustainability, 127–36. New York, NY : Routledge, 2021. | Series: Human centered management: Routledge, 2021. http://dx.doi.org/10.4324/9781003094463-9-13.

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Degen, Helmut, and Stavroula Ntoa. "From a Workshop to a Framework for Human-Centered Artificial Intelligence." In Artificial Intelligence in HCI, 166–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77772-2_11.

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Jensen, Theodore. "Disentangling Trust and Anthropomorphism Toward the Design of Human-Centered AI Systems." In Artificial Intelligence in HCI, 41–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77772-2_3.

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Pacailler, Matthew, Sarah Yahoodik, Tetsuya Sato, Jeremiah G. Ammons, and Jeremiah Still. "Human-Centered Artificial Intelligence: Beyond a Two-Dimensional Framework." In Lecture Notes in Computer Science, 471–82. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21707-4_33.

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Fischer, Gerhard. "Rethinking and reinventing Artificial Intelligence from the perspective of human-centered computational artifacts." In Advances in Artificial Intelligence, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0034793.

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Qian, Ming, and Davis Qian. "Defining a Human-Machine Teaming Model for AI-Powered Human-Centered Machine Translation Agent by Learning from Human-Human Group Discussion: Dialog Categories and Dialog Moves." In Artificial Intelligence in HCI, 70–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50334-5_5.

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Komischke, Tobias. "Human-Centered Artificial Intelligence Considerations and Implementations: A Case Study from Software Product Development." In Artificial Intelligence in HCI, 260–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77772-2_17.

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Conference papers on the topic "Human-Centered Artificial Intelligence"

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Schmidt, Albrecht. "Interactive Human Centered Artificial Intelligence." In AVI '20: International Conference on Advanced Visual Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3399715.3400873.

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Ando, Masaya, and Yasunobu Ito. "Conceptual Change in Human-centered Design by Artificial Intelligence System." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002552.

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It has been pointed out that the development of artificial intelligence can transform the relationship between systems and people fundamentally. Human-centered design (HCD) is a concept that realizes a human-centered system by grasping user usage and deriving user requirements. This design concept is still important and is effective when systematizing existing operations. However, when a user's advanced work can be replaced by artificial intelligence, the relationship between the user and the system changes dramatically, but the direction has not been clarified. In this study, interviews were conducted with service providers on the site using artificial intelligence and discussed the necessity of rebuilding the meaning and significance of the operations and actions of the user using the system.
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Valveny, Ernest, and Ramon Vilanova. "TRANSVERSAL APPROACH TO CURRICULUM DESIGN - HUMAN-CENTERED ARTIFICIAL INTELLIGENCE." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.2469.

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Frejus, Myriam, Dounia Lahoual, and Marion Gras-Gentiletti. "Making Human-AI Interactions Sustainable: 7 Key Questions for an Ergonomics Perspective on Artificial Intelligence." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001948.

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Recent advances in artificial intelligence (AI) have led to new forms of human-AI interaction and confirmed the need for human-centered AI design. But what are the human factors that need to be addressed for successful AI design? This paper looks at seven key questions critical to designing human-AI interactions in a sustainable way. It also examines recent and emerging factors in relation to challenges posed by earlier forms of automation and AI (such as expert systems). The aim of our research is to propose a framework for multidisciplinary efforts essential to human-centered intelligent system design; it identifies potential activity centered ergonomics contributions and the issues that need to be addressed through situated studies of sociotechnical systems and human activities.
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Zhang, Ruohan, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, and Mary Hayhoe. "Human Gaze Assisted Artificial Intelligence: A Review." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/689.

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Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.
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Schneider, Bettina, Petra Asprion, and Frank Grimberg. "Human-centered Artificial Intelligence: A Multidimensional Approach towards Real World Evidence." In 21st International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007715503810390.

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Bilda, Kerstin, Fiona Dörr, and Anika Thurmann. "Artificial intelligence solutions in Parkinson therapy." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001037.

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Parkinson's disease (PD) is the second most common neurodegenerative disease in Europe [1]. PD requires a high-frequency therapy offer for a sustainable improvement of the communicative abilities. To improve speech therapy care in PD, technology-based therapies can be useful, essential in these digital health applications is that objective feedback is available for correct exercise performance. The "Speech" application from the ISi-Speech research project [1] provides technology-based training for the therapy of dysarthria in PD. The overall goal of the application is to improve speech functions and thus promote participation and quality of life for those affected. Methodologically, a strictly user-centered implementation approach is planned. Therapists* implement ISi-Speech into an existing evidenced based therapy format. Assumptions about the development of digital maturity levels among therapists*, best practice models for inpatient and outpatient therapy settings and implementation strategies will be identified through the feasibility study. The intervention is part of a public project of the Federal Ministry of Education and Research in Germany. The project humAine - human centered AI network focuses on different areas of business and healthcare. This specific use case is about the implementation of AI in speech therapy with the aim to sustainably simplify the workflow, identify competency strategies and identify best-practice models.
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Maria Arnaldo Valdés, Rosa, Victor Fernando Gómez Comendador, Raquel Delgado-Aguilera, Francisco Perez Moreno, and Maria Zamarreño. "Impact of Artificial Intelligence in the Certification of Human-Centered Aviation systems." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001432.

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In recent years we have witnessed the emergence of applications based on artificial intelligence in the aviation industry. This technology is said to be promoting a new era or evolution, such as the introduction of jet engines in the 1950s and fly-by-wire in the 1980s. To maintain aviation safety standards in this transition, civil aviation authorities responsible for certifying aerospace systems must anticipate the unprecedented impact of AI on human-centric aerospace systems and answer a number of critical questions:•How to establish public trust in human- centric AI-based systems?•How to integrate the ethical dimension of human- centric AI (transparency, non-discrimination, fairness, etc.) in safety certification processes? •How to prepare for the certification of human- centric AI systems? •What standards, protocols, methods needs to be developed to ensure that human- centric AI further improves the current level of air transport safety? EASA, the European Aviation Safety Agency, has recently developed a roadmap for the certification of AI applications in aviation, which analyzes the involvement of human- centric AI in the aviation sector and identifies the objectives that must be met and the actions that must be taken to respond to the previous questions. This effort constitutes a starting point for the certification of human- centric AI in aerospace systems. It develops in particular the core notion of trustworthiness of human- centric AI in human centered systems and proposes a framework based on four human- centric AI trustworthiness building block:— trustworthiness analysis — learning assurance — explainability — safety risk mitigation The presented paper syntheses the concept of human- centric AI applications, it also discusses and revises the 4 elements of the trustworthiness of human- centric AI framework proposed by EASA, and based on this discussion anticipates the possible impacts of the introduction of human- centric AI in the different Implementation Rules (IR), Certification Specifications (CS), Acceptable Means of Compliance (AMC) and guidance material (GM) in the domains covered by the EASA Basic Regulation.
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Hepenstal, Sam, Leishi Zhang, and B. L. William Wong. "An analysis of expertise in intelligence analysis to support the design of Human-Centered Artificial Intelligence." In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021. http://dx.doi.org/10.1109/smc52423.2021.9659095.

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Gupta, Binay, Anirban Chatterjee, Subhadip Paul, Matha Harika, Lalitdutt Parsai, Kunal Banerjee, and Vijay Agneeswaran. "Look before You Leap! Designing a Human-centered AI System for Change Risk Assessment." In 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010877500003116.

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