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 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 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 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.

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.

The Ethics of Artificial Intelligence has two goals. The first goal is meta-theoretical and is fulfilled by Part One, which comprises the first three chapters: an interpretation of the past (Chapter 1), the present (Chapter 2), and the future of AI (Chapter 3). Part One develops the thesis that AI is an unprecedented divorce between agency and intelligence. On this basis, Part Two investigates the consequences of such a divorce, developing the thesis that AI as a new form of agency can be harnessed ethically and unethically. It begins (Chapter 4) by offering a unified perspective on the many principles that have been proposed to frame the ethics of AI. This leads to a discussion (Chapter 5) of the potential risks that may undermine the application of these principles, and then (Chapter 6) an analysis of the relation between ethical principles and legal norms, and a definition of soft ethics as post-compliance ethics. Part Two continues by analysing the ethical challenges caused by the development and use of AI (Chapter 7), evil uses of AI (Chapter 8), and good practices when applying AI (Chapter 9). The last group of chapters focuses on the design, development, and deployment of AI for Social Good or AI4SG (Chapter 10); the positive and negative impacts of AI on the environment and how it can be a force for good in the fight against climate change-but not without risks and costs, which can and must be avoided or minimised (Chapter 11); and the possibility of using AI in support of the United Nations Sustainable Development Goals (Chapter 12). The book concludes (Chapter 13) by arguing in favour of a new marriage between the Green of all our habitats and the Blue of all our digital technologies and how this new marriage can support and develop a better society and a healthier biosphere.