具有社会责任的人工智能：理论与实践 Socially Responsible Ai: Theories And Practices
基本信息
Format Hardback | 196 pages
Publication date 06 Apr 2023
Publisher World Scientific Publishing Co Pte Ltd
ISBN10 981126662X
ISBN13 9789811266621
页面参数仅供参考，具体以实物为准
书籍简介
在当今时代，人们和社会对人工智能（AI）技术的依赖性越来越强。人工智能有可能推动我们走向一个全人类繁荣的未来。它也伴随着压迫和灾祸的巨大风险。作为回应，研究人员和组织一直在努力发布原则，并制定人工智能法规，以便在相应的应用领域负责任地使用人工智能。然而，这些理论上制定的原则和法规也需要转化为可操作的算法，以实现人工智能的美好。
本书引入了社会责任人工智能的统一视角，以帮助将概念性的人工智能原则与负责任的人工智能实践联系起来。它从对社会责任人工智能的跨学科定义和人工智能责任金字塔开始。然后介绍了寻求实现主流责任人工智能原则的现有努力。本书还讨论了如何利用先进的人工智能技术，通过 "保护"、"告知 "和 "预防 "来解决具有挑战性的社会问题，并以开放的问题和挑战作为结尾。
本书是研究人员、从业人员和学生了解对社会负责的人工智能的问题和挑战的一个方便的切入点，并确定他们的专业领域如何能够为使人工智能对社会负责作出贡献。
In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good.
This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges.
This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible.
作者简介
By (author): Lu Cheng (University of Illinois at Chicago, USA) and Huan Liu (Arizona State University, USA)
程璐是美国伊利诺伊大学芝加哥分校（UIC）计算机科学系的终身助理教授。他的研究重点是利用统计学和因果关系的方法，将概念性的人工智能原则与负责任的人工智能实践联系起来。Lu在SIGKDD知识发现和数据挖掘会议（KDD）、ACM网络搜索和数据挖掘国际会议（WSDM）、美国人工智能促进会（AAAI）、国际人工智能联合会议（IJCAI）和计算语言学协会（ACL）年会等会议上发表文章。她是WSDM'22的网络主席和AAAI'22-23的高级程序委员会成员。陆晓明曾获得2022年计算机与增强智能学院计算机科学博士优秀学生奖，2021年亚利桑那州立大学工程学院院长论文奖，2020年亚利桑那州立大学研究生优秀研究奖，以及IBM博士社会公益奖学金。
Lu Cheng is a tenure-track assistant professor in Computer Science at University of Illinois at Chicago (UIC), USA. Lu's research focuses on bridging conceptual AI principles to responsible AI practice using both statistical and causality-aware methods. Lu has published in SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), ACM International Conference on Web Search and Data Mining (WSDM), Association for the Advancement of Artificial Intelligence (AAAI), International Joint Conference on Artificial Intelligence (IJCAI), and Annual Meeting of the Association for Computational Linguistics (ACL), among others. She is the web chair of WSDM'22 and senior program committee member of AAAI'22–23. Lu was the recipient of the 2022 Computer Science PhD Outstanding Student Award in the School of Computing and Augmented Intelligence, 2021 Arizona State University Engineering Dean's Dissertation Award, 2020 Arizona State University Graduate Outstanding Research Award, and IBM PhD Social Good Fellowship.
目录
Preface
About the Authors
Acknowledgments
Defining Socially Responsible AI
Theories in Socially Responsible AI
Practices of Socially Responsible AI
Challenges of Socially Responsible AI
Bibliography
Index
Deep Learning in Medical Imaging and Its Challenges (Xianju Wang)
