AI and Sustainable Transformations
Gyan Prakash, Amandeep Kaur
About this book
AI and Sustainable Transformations presents cutting-edge research and insights into the role of Artificial Intelligence (AI) and emerging technologies in driving human-centered inno- vation and sustainable transformation across various industries. With an emphasis on aligning technological advancements with sustainable practices, this book explores applications of AI in manufacturing, healthcare and the food industry, it delves into critical themes such as Internet of Things-enabled smart manufacturing, block- chain for secure industrial ecosystems, emerging technologies for sustainable futures, intel- ligent healthcare systems, AI for sustainable and healthcare supply chains, and applications of AI in the food industry. Through in-depth case studies, technical analyses and alternative solutions, readers will gain a broad-based perspective on AI-driven human-centric development. This is an essential resource for academics, researchers, professionals and policy makers who wish to leverage innovative strategies for a better tomorrow.
Questions & Answers from this book
Questions and answers are connected to the referenced book and its available source material.
Chapter Section 2: Insights, ethics and frameworks in responsible AI
According to the chapter, what limitations do AI systems have in achieving moral values, and what is needed to address ethical questions in AI research?
AI systems have intrinsic limitations in achieving moral values: it is difficult to model ethical reasoning about obligations and permissions in machines, ethics involves prescriptions that are not based on physical observations, and many specific ethical questions are difficult even for humans. To address ethical questions in AI research, a well-designed multidisciplinary framework is needed, along with supports such as Ethics as a Service, ethical design training for developers, and repeated ethics education.
What is the difference between the conventional lift metric and the weighted lift metric used in ProWAR?
Conventional lift ranks association rules purely on statistical co-occurrence, measuring how much more often items appear together than expected by chance. ProWAR's weighted lift, shown as Revenue Lift Optimized, adds profit margins and quantity weights through the Revenue Index, so rules that link high-margin, high-demand items are promoted even if their raw lift is not the highest.
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