This annual edited volume presents an overview of cutting-edge research areas within digital ethics as defined by the Digital Ethics Lab of the University of Oxford. It identifies new challenges and opportunities of influence in setting the research agenda in the field. The 2020 edition of the yearbook presents research on the following topics: governing digital health, visualising governance, the digital afterlife, the possibility of an AI winter, the limits of design theory in philosophy, cyberwarfare, ethics of online behaviour change, governance of AI, trust in AI, and Emotional Self-Awareness as a Digital Literacy. This book appeals to students, researchers and professionals in the field.

This edited volume presents an overview of cutting-edge research areas within digital ethics as defined by the Digital Ethics Lab of the University of Oxford. It identifies new challenges and opportunities of influence in setting the research agenda in the field. The yearbook presents research on the following topics: conceptual metaphor theory, cybersecurity governance, cyber conflicts, anthropomorphism in AI, digital technologies for mental healthcare, data ethics in the asylum process, AI’s legitimacy and democratic deficit, digital afterlife industry, automatic prayer bots, foresight analysis and the future of AI. This volume appeals to students, researchers and professionals.

This annual edited volume explores a wide range of topics in digital ethics and governance. Included are chapters that: analyze the opportunities and ethical challenges posed by digital innovation; delineate new approaches to solve them; and offer concrete guidance on how to govern emerging technologies. The contributors are all members of the Digital Ethics Lab (the DELab) at the Oxford Internet Institute, a research environment that draws on a wide range of academic traditions. Collectively, the chapters of this book illustrate how the field of digital ethics - whether understood as an academic discipline or an area of practice - is undergoing a process of maturation. Most importantly, the focus of the discourse concerning how to design and use digital technologies is increasingly shifting from ‘soft ethics’ to ‘hard governance’. Then, there is the trend in the ongoing shift from ‘what’ to ‘how’, whereby abstract or ad-hoc approaches to AI governance are giving way to more concrete and systematic solutions. The maturation of the field of digital ethics has, as this book attempts to show, been both accelerated and illustrated by a series of recent events. This text thereby takes an important step towards defining and implementing feasible and effective approaches to digital governance. It appeals to students, researchers and professionals in the field.

This book explores a wide range of topics in digital ethics. It features 11 chapters that analyze the opportunities and the ethical challenges posed by digital innovation, delineate new approaches to solve them, and offer concrete guidance to harness the potential for good of digital technologies. The contributors are all members of the Digital Ethics Lab (the DELab), a research environment that draws on a wide range of academic traditions. The chapters highlight the inherently multidisciplinary nature of the subject, which cannot be separated from the epistemological foundations of the technologies themselves or the political implications of the requisite reforms. Coverage illustrates the importance of expert knowledge in the project of designing new reforms and political systems for the digital age. The contributions also show how this task requires a deep self-understanding of who we are as individuals and as a species. The questions raised here have ancient -- perhaps even timeless -- roots. The phenomena they address may be new. But, the contributors examine the fundamental concepts that undergird them: good and evil, justice and truth. Indeed, every epoch has its great challenges. The role of philosophy must be to redefine the meaning of these concepts in light of the particular challenges it faces. This is true also for the digital age. This book takes an important step towards redefining and re-implementing fundamental ethical concepts to this new era.

This annual edited volume presents an overview of cutting-edge research areas within digital ethics as defined by the Digital Governance Research Group of the University of Oxford. It identifies new challenges and opportunities of influence in setting the research agenda in the field. The 2022 edition of the Yearbook presents research on the following topics: autonomous weapons, cyber weapons, digital sovereignty, smart cities, artificial intelligence for the Sustainable Development Goals, vaccine passports, and sociotechnical pragmatism as an approach to technology. This text appeals to students, researchers, and professionals in the field.

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.

This book maps out the moral, legal and societal issues brought forth by the use of autonomous systems such as AI and smart robots in outer space. Humanity is on the brink of a new space era in which projects for permanent human colonies on the Moon and space missions with autonomous AI systems will soon become a reality. Principles and provisions of international space law fall increasingly short in tackling this scenario. Experts and institutions have recommended improvements to the legal framework, such as new international agreements, or policies that would not require any amendment to conventional law. Most of the time, such proposals and recommendations overlook the challenges posed by technology and how autonomous and intelligent systems in outer space require moral and legal standards of their own. This book argues that the traditional focus on satellite communications, space-related services, and the appropriability of celestial resources needs to be integrated by new laws of outer space regulating cybersecurity law and environmental law, data governance and consumer protection. The new laws of outer space will increasingly concern the development of new standards for the behaviour and decision-making of AI systems and smart robots, with and without humans aboard deep space missions and in next-generation colonies. What laws shall govern us out there, in a new terra incognita? This is the question that the book sets out to answer.

Artificial Intelligence and Machine Learning for Women's Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to women's health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on women's health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women's health issues. - Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women's health issues - Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities - Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women's healthcare

A short, thought-provoking book about what happens to our online identities after we die. These days, so much of our lives takes place online—but what about our afterlives? Thanks to the digital trails that we leave behind, our identities can now be reconstructed after our death. In fact, AI technology is already enabling us to “interact” with the departed. Sooner than we think, the dead will outnumber the living on Facebook. In this thought-provoking book, Carl Öhman explores the increasingly urgent question of what we should do with all this data and whether our digital afterlives are really our own—and if not, who should have the right to decide what happens to our data. The stakes could hardly be higher. In the next thirty years alone, about two billion people will die. Those of us who remain will inherit the digital remains of an entire generation of humanity—the first digital citizens. Whoever ends up controlling these archives will also effectively control future access to our collective digital past, and this power will have vast political consequences. The fate of our digital remains should be of concern to everyone—past, present, and future. Rising to these challenges, Öhman explains, will require a collective reshaping of our economic and technical systems to reflect more than just the monetary value of digital remains. As we stand before a period of deep civilizational change, The Afterlife of Data will be an essential guide to understanding why and how we as a human race must gain control of our collective digital past—before it is too late.

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.