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Full Description
This collection of articles and interviews surveys human-centered approaches to machine learning that can make AI more human-friendly, usable, and ethical. It provides a handbook for students, researchers, and practitioners who want new ways of approaching AI that place humanity at their center. It shows how to apply methods from human-computer interaction that have enabled computing technology to become user-friendly and human-centric to the new technologies of AI and machine learning. The book has 13 articles and 9 interviews from a range of different perspectives, helping readers understand existing machine learning systems and their impacts on people and society. It is an ideal introduction both for human-computer interaction practitioners who are interested in working with machine learning and for machine learning experts interested in making their practice more human-centered. The book offers a critical lens on existing machine learning alongside an optimistic vision of AI in the service of humanity.
Contents
1. Introduction Rebecca Fiebrink, Marco Gillies and Gonzalo Ramos; Part I. Human-Centered Machine Learning in the Arts and Humanities: 2. Interviews: people Rebecca Fiebrink, Marco Gillies and Gonzalo Ramos; 3. Humanities and human-centered machine learning David Mimno and Laure Thompson; 4. Machine learning in movement-based interaction for performing arts applications Frederic Bevilacqua, Jules Françoise, Sarah Fdili Alaoui and Baptiste Caramiaux; Part II. Doing Human-Centered Machine Learning: 5. Measuring the user experience of human-centered machine learning: trust, intelligibility, and more Simone Stumpf and Alison Smith-Renner; 6. Data for machine learning: a human-centered perspective of crowdsourcing Edith Law and Ming Yin; 7. REDesigning AI products and services: benefits, challenges, and ideas for improving design practice Jodi Forlizzi, John Zimmerman, Qian Yang, Changhoon Oh and Nur Yildirim; Part III. Humans and Machine Learning: 8. Human-centered explainable AI (XAI): from algorithms to user experiences Q Vera Liao and Kush R. Varshney; 9. Humans teaching machine learning agents Samantha Krening; 10. Modeling the social and normative context of human-agent interactions Marynel Vìazquez, Hatice Gunes, Tom Williams and Ryan Blake Jackson; 11. Interviews: teams Rebecca Fiebrink, Marco Gillies and Gonzalo Ramos; Part IV. Machine Learning in Its Social Context: 12. A use-appraise-modify-create learning progression (UAMC) and a machine learning education framework (MLEF) for the development of AI-engaged citizens Natalie Lao and Irene Lee; 13. Artificially intelligent technology for the margins: reflecting on challenges and opportunities Tanja Aal, Jasmin Niess, Konstantin Aal, Douglas Zytko, Soaad Hossain, Giovanna Nunes Vilaza, Reem Talhouk, Heloisa Caroline de Souza Pereira Candello, Evangelos Kapros, Maria Koutsombogera, Franziska Tachtler, Daniel Diethei, Mohammed Khwaja, Shaimaa Lazem, Aneesha Singh, Marguerite Barry, Geraldine Fitzpatric, Volker Wulf and Claudia Müller; 14. The anatomy of the saliency cropping leviathan Vinay Uday Prabhu and Abeba Birhane; Part V. Machine Learning and Humanity: 15. Interviews: humanity Rebecca Fiebrink, Marco Gillies and Gonzalo Ramos; 16. Conclusion Rebecca Fiebrink, Marco Gillies and Gonzalo Ramos.
