Book edited by Fang Chen and Jianlong Zhou.
Published by Springer.
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.
Provides a systematic view of humanity benefits that AI can provide from the perspectives of productivity, well-being, sustainability, and partnership.
About the Editors
- Fang 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.
- Jianlong 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.