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Книги з теми "Human-Centered Artificial Intelligence"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

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

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2

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

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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Holzinger, Andreas. HCI and Usability for e-Inclusion: 5th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society, USAB 2009, Linz, Austria, November 9-10, 2009 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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12

Kuflik, Tsvi. Advances in ubiquitous user modelling: Revised selected papers. Berlin: Springer, 2009.

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13

Human-Centered Artificial Intelligence. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-02460-6.

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14

Lee, Sangwon, Jae-Yoon Jung, and Chang S. Nam. Human-Centered Artificial Intelligence. Elsevier Science & Technology Books, 2022.

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15

Golight, Nastaran Dadashi David. Human Centered Intelligent Infrastructure. Taylor & Francis Group, 2021.

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16

Lee, Sangwon, Jae-Yoon Jung, and Chang S. Nam. Human-Centered Artificial Intelligence: Research and Applications. Elsevier Science & Technology, 2022.

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17

Boy, Guy. Orchestrating Human-Centered Design. Springer London, Limited, 2014.

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18

Fu, Yun. Human-Centered Social Media Analytics. Springer, 2014.

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19

Fu, Yun. Human-Centered Social Media Analytics. Springer, 2016.

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20

Fu, Yun. Human-Centered Social Media Analytics. Springer London, Limited, 2014.

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21

Shneiderman, Ben. Human-Centered AI. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845290.001.0001.

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Анотація:

Researchers, developers, business leaders, policy makers, and others are expanding the technology-centered scope of artificial intelligence (AI) to include human-centered AI (HCAI) ways of thinking. This expansion from an algorithm-focused view to embrace a human-centered perspective can shape the future of technology so as to better serve human needs. Educators, designers, software engineers, product managers, evaluators, and government agency staffers can build on AI-driven technologies to design products and services that make life better for people and enable people to care for each other. Humans have always been tool builders, and now they are supertool builders, whose inventions can improve our health, family life, education, business, the environment, and much more. The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits AI researchers, developers, business leaders, policy makers, and others who build on their working methods by including HCAI strategies of design and testing. This enlarged vision can shape the future of technology so as to better serve human needs. As many technology companies and thought leaders have said, the goal is not to replace people, but to empower them by making design choices that give humans control over technology.

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22

Kanter, Beth, and Allison H. Fine. Smart Nonprofit: The Human-Centered Approach to Managing Artificial Intelligence for Social Good. Wiley & Sons, Incorporated, John, 2022.

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23

Kanter, Beth, and Allison H. Fine. Smart Nonprofit: The Human-Centered Approach to Managing Artificial Intelligence for Social Good. Wiley & Sons, Incorporated, John, 2022.

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24

Rosenbrock, H. H. Designing Human-Centered Technology: A Cross-Disciplinary Project in Computer-Aided Manufacturing (Artificial Intelligence and Society). Springer, 1990.

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25

Kaci, Souhila. Working with Preferences: Less Is More. Springer, 2011.

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26

Masuda, Yuji. Human-Centered Systems in the Global Economy: Proceedings from the International Workshop on Industrial Cultures and Human-Centered Systems Held by T (Artificial Intelligence and Society). Springer, 1992.

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27

Orchestrating Humancentered Design. Springer, 2012.

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28

Kaci, Souhila. Working with Preferences: Less Is More. Springer, 2013.

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29

Kaci, Souhila. Working with Preferences: Less Is More. Springer, 2011.

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30

Forbrig, Peter, Regina Bernhaupt, and Marco Winckler. Human-Centered Software Engineering: 4th International Conference, HCSE 2012, Toulouse, France, October 29-31, 2012, Proceedings. Springer Berlin / Heidelberg, 2012.

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31

Tang, Yong, Qiaohong Zu, and José G. Rodríguez García. Human Centered Computing: 4th International Conference, HCC 2018, Mérida, Mexico, December, 5–7, 2018, Revised Selected Papers. Springer, 2019.

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32

Hu, Bo, and Qiaohong Zu. Human Centered Computing: Third International Conference, HCC 2017, Kazan, Russia, August 7–9, 2017, Revised Selected Papers. Springer, 2018.

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33

Hu, Bo, and Qiaohong Zu. Human Centered Computing: Second International Conference, HCC 2016, Colombo, Sri Lanka, January 7-9, 2016, Revised Selected Papers. Springer, 2016.

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34

Hu, Bo, and Qiaohong Zu. Human Centered Computing: Second International Conference, HCC 2016, Colombo, Sri Lanka, January 7-9, 2016, Revised Selected Papers. Springer London, Limited, 2016.

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35

Hu, Bo, Qiaohong Zu, Ning Gu, and Sopheap Seng. Human Centered Computing: First International Conference, HCC 2014, Phnom Penh, Cambodia, November 27-29, 2014, Revised Selected Papers. Springer London, Limited, 2015.

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36

Human-Centered Software Engineering: 5th IFIP WG 13. 2 International Conference, HCSE 2014, Paderborn, Germany, September 16-18, 2014. Proceedings. Springer Berlin / Heidelberg, 2014.

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37

Forbrig, Peter, Stefan Sauer, Regina Bernhaupt, Marco Winckler, and Cristian Bogdan. Human-Centered Software Engineering: 5th IFIP WG 13. 2 International Conference, HCSE 2014, Paderborn, Germany, September 16-18, 2014. Proceedings. Springer London, Limited, 2014.

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38

Winckler, Marco, Cristian Bogdan, Philippe Palanque, Kati Kuusinen, and Marta Kristín Lárusdóttir. Human-Centered Software Engineering: 7th IFIP WG 13. 2 International Working Conference, HCSE 2018, Sophia Antipolis, France, September 3-5, 2018, Revised Selected Papers. Springer International Publishing AG, 2018.

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39

(Editor), Allen Kent, and James G. Williams (Editor), eds. Encyclopedia of Computer Science and Technology: Volume 40 - Supplement 25 - An Approach to Complexity from a Human-Centered Artificial Intelligence Perspective ... of Computer Science and Technology). CRC, 1999.

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40

Schmorrow, Dylan D., and Cali M. Fidopiastis. Foundations of Augmented Cognition. Directing the Future of Adaptive Systems: 6th International Conference, FAC 2011, Held as Part of HCI ... FL, USA, July 9-14, 2011, Proceedings. Springer, 2011.

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41

Hoffmann, Rüdiger, Anna Esposito, Alessandro Vinciarelli, Vincent C. Müller, and Antonietta M. Esposito. Cognitive Behavioural Systems: COST 2102 International Training School, Dresden, Germany, February 21-26, 2011, Revised Selected Papers. Springer, 2012.

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42

Esposito, Anna, Alessandro Vinciarelli, and Antonietta M. Esposito. Cognitive Behavioural Systems: COST 2102 International Training School, Dresden, Germany, February 21-26, 2011, Revised Selected Papers. Springer, 2012.

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43

(Editor), Cristina Conati, Kathleen McCoy (Editor), and Georgios Paliouras (Editor), eds. User Modeling 2007: 11th International Conference, UM 2007, Corfu, Greece, July 25-29, 2007, Proceedings (Lecture Notes in Computer Science). Springer, 2007.

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44

Holzinger, Andreas, and Klaus Miesenberger. HCI and Usability for E-Inclusion. Springer, 2009.

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45

Heckmann, Dominikus, Tsvi Kuflik, Shlomo Berkovsky, Antonio Krüger, and Francesca Carmagnola. Advances in Ubiquitous User Modelling: Revised Selected Papers. Springer London, Limited, 2009.

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46

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Анотація:

Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.

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47

Hembrooke, Helene, and Geraldine Gay. Activity-Centered Design: An Ecological Approach to Designing Smart Tools and Usable Systems. MIT Press, 2018.

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48

Activity-Centered Design: An Ecological Approach to Designing Smart Tools and Usable Systems (Acting with Technology). The MIT Press, 2004.

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49

Estlund, Cynthia. Automation Anxiety. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197566107.001.0001.

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Анотація:

This book confronts the hotly debated prospect of mounting job losses from automation, and the divergent hopes and fears that prospect evokes, and proposes a strategy for mitigating the losses and spreading the gains from shrinking demand for human labor. Leading economists have concluded that automation is already exacerbating inequality by destroying more decent middle-skill jobs than it is creating. As ongoing innovations in artificial intelligence, machine learning, and robotics continue to chip away at the comparative advantages of human labor in a range of work tasks, those innovations are likely to yield growing job losses in the foreseeable future—or likely enough that we should reckon with this prospect. The book argues that we should set our collective sights on ensuring broad access to adequate incomes, more free time, and decent remunerative work even in a world with less of it. That will require not a single “magic bullet” solution like universal basic income or a federal job guarantee, but rather a multifaceted strategy centered on conserving, creating, and spreading work. The book elaborates that strategy in the US context, but much of it is broadly relevant to other advanced economies. And while the proposed strategy is designed to address a foreseeable future of job scarcity, it will also help to rebalance lives already plagued by either too much work or not enough and to counter both economic inequality and racial stratification. The proposed strategy makes sense here and now, and especially as we face up to a future of less work.

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