Humanity Driven AI

Productivity, Well-being, Sustainability and Partnership
1. Auflage 2022
ISBN: 978-3-030-72190-9
Verlag: Springer

Buch, Englisch, 330 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 522 g

Artificial Intelligence (AI) is changing the world around us, and it is changing the way people are living, working, and entertaining. As a result, demands for understanding how AI functions to achieve and enhance human goals from basic needs to high level well-being (whilst maintaining human health) are increasing. This edited book systematically investigates how AI facilitates enhancing human needs in the digital age, and reports on the state-of-the-art advances in theories, techniques, and applications of humanity driven AI. Consisting of five parts, it covers the fundamentals of AI and humanity, AI for productivity, AI for well-being, AI for sustainability, and human-AI partnership.

Humanity Driven AI creates an important opportunity to not only promote AI techniques from a humanity perspective, but also to invent novel AI applications to benefit humanity. It aims to serve as the dedicated source for the theories, methodologies, and applications on humanity driven AI, establishing state-of-the-art research, and providing a ground-breaking book for graduate students, research professionals, and AI practitioners.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Fang

Zhou, Jianlong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein Soziale und ethische Aspekte der EDV

Inhaltsverzeichnis

Part I           AI and Humanity Chapter 1   Towards Humanity-in-The-Loop in AI Lifecycle

Jianlong Zhou and Fang Chen

Chapter 2 AI and Ethics --- Operationalising Responsible AI

Liming Zhu, Xiwei Xu, Qinghua Lu, Guido Governatori, and Jon Whittle

Part II          AI for ProductivityChapter 3  Machine Learning for Efficient Water Infrastructure Management

Zhidong Li, Bin Liang, and Yang Wang

Chapter 4 AI for Real-Time Bus Travel Time Prediction in Traffic Congestion Management

Yuming Ou

Chapter 5 The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques

Boyu Li, Ting Guo, Yang Wang, and Fang Chen

Part III         AI for WellbeingChapter 6  Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Guodong Long, Tao Shen, Yue Tan, Leah Gerrard and Jing Jiang

Chapter 7 AI-Enhanced 3D Biomedical Data Analytics for Neuronal Structure Reconstruction

Heng Wang, Yang Song, Zihao Tang, Chaoyi Zhang, Jianhui Yu, Dongnan Liu, Donghao Zhang, Siqi Liu, and Weidong Cai

Chapter 8 Artificial Intelligence for Fighting the COVID-19 Pandemic

Rohit Salgotra, Iman Rahimi, and Amir H Gandomi

Part IV         AI for SustainabilityChapter 9  Sewer Corrosion Prediction for Sewer Network Sustainability

Jianjia Zhang, Bin Li, Xuhui Fan, Yang Wang, and Fang Chen

Chapter 10  AI Applied to Air Pollution and Environmental Health: A Case Study on Hypothesis Generation

Colin Bellinger, Mohomed Shazan Mohomed Jabbar, Osnat Wine, Charlene. Nielsen, Jesus Serrano-Lomelin,  Alvaro Osornio-Vargas, and Osmar R. Zaiane

Chapter 11 SharkSpotter: Shark Detection with Drones for Human Safety and Environmental Protection

Nabin Sharma, Muhammed Saqib, Paul Scully-Power, and Michael Blumenstein

 Part V          AI + Human PartnershipChapter 12   Learner Engagement Examination via Computer Usage Behaviors

Kun Yu, Jie Xu, Yuming Ou, Ling Luo, and Fang Chen

Chapter 13 Virtual Teaching Assistants: Technologies, Applications and Challenges

Jun Liu, Lingling Zhang, Bifan Wei, and Qinghua Zheng

Chapter 14   Artificial Intelligence and People with Disabilities: A Reflection on Human-AI Partnerships

Jason J.G. White

Chapter 15   Towards a Taxonomy for Explainable AI in Computational Pathology

Heimo Mueller, Michaela Kargl, Markus Plass, Bettina Kipperer, Luka Brcic, Peter Regitnig, Christian Geissler, Tobias Kuester, Norman Zerbe and Andreas Holzinger

Über Autor(innen)

Dr Chen is a prominent leader in data science with an international reputation and industrial recognitions. She has created many innovative research and solutions, transforming industries that utilise data science.Dr Chen and her team  won the 2018 Australian leading science prize Australian Museum Eureka Prize for Excellence in Data Science. 
Dr. Zhou is a leading senior researcher in trustworthy and transparent machine learning, and has done pioneering research in the area of linking human and machine learning. He also works with industries in advanced data analytics for transforming data into actionable operations particularly by incorporating human user aspects into machine learning and translate machine learning into impacts in real world applications.