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This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
The 21st century is on the verge of a possible total economic and political revolution. Technological advances in robotics, computing and digital communications have the potential to completely transform how people live and work. Even more radically, humans will soon be interacting with artificial intelligence (A.I.) as a normal and essential part of their daily existence. What is needed now more than ever is to rethink social relations to meet the challenges of this soon-to-arrive "smart" world. This book proposes an original theory of trans-human relations for this coming future. Drawing on insights from organisational studies, critical theory, psychology and futurism - it will chart for readers the coming changes to identity, institutions and governance in a world populated by intelligent human and non-human actors alike. It will be characterised by a fresh emphasis on infusing programming with values of social justice, protecting the rights and views of all forms of "consciousness" and creating the structures and practices necessary for encouraging a culture of "mutual intelligent design". To do so means moving beyond our anthropocentric worldview of today and expanding our assumptions about the state of tomorrow's politics, institutions, laws and even everyday existence. Critically such a profound shift demands transcending humanist paradigms of a world created for and by humans and instead opening ourselves to a new reality where non-human intelligence and cyborgs are increasingly central.
This book offers a synthesis of investigations on the ethics, governance and policies affecting the design, development and deployment of artificial intelligence (AI). Each chapter can be read independently, but the overall structure of the book provides a complementary and detailed understanding of some of the most pressing issues brought about by AI and digital innovation. Given its modular nature, it is a text suitable for readers who wish to gain a reliable orientation about the ethics of AI and for experts who wish to know more about specific areas of the current debate.
"Book abstract: The Oxford Handbook of AI Governance examines how artificial intelligence (AI) interacts with and influences governance systems. It also examines how governance systems influence and interact with AI. The handbook spans forty-nine chapters across nine major sections. These sections are (1) Introduction and Overview, (2) Value Foundations of AI Governance, (3) Developing an AI Governance Regulatory Ecosystem, (4) Frameworks and Approaches for AI Governance, (5) Assessment and Implementation of AI Governance, (6) AI Governance from the Ground Up, (7) Economic Dimensions of AI Governance, (8) Domestic Policy Applications of AI, and (9) International Politics and AI"--
Attention in the AI safety community has increasingly started to include strategic considerations of coordination between relevant actors in the field of AI and AI safety, in addition to the steadily growing work on the technical considerations of building safe AI systems. This shift has several reasons: Multiplier effects, pragmatism, and urgency. Given the benefits of coordination between those working towards safe superintelligence, this book surveys promising research in this emerging field regarding AI safety. On a meta-level, the hope is that this book can serve as a map to inform those working in the field of AI coordination about other promising efforts. While this book focuses on AI safety coordination, coordination is important to most other known existential risks (e.g., biotechnology risks), and future, human-made existential risks. Thus, while most coordination strategies in this book are specific to superintelligence, we hope that some insights yield “collateral benefits” for the reduction of other existential risks, by creating an overall civilizational framework that increases robustness, resiliency, and antifragility.
Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important
A data model represents a precise information landscape, and there are different levels of modeling depending on the audience and the model's purpose. A conceptual data model (CDM) is the highest-level of modeling and is designed to capture business needs and help both business and IT professionals agree on a common set of terms and definitions. It is an extremely powerful data model and this video will not only explain the CDM, but also work with you on a hands-on exercise covering the five steps for creating a CDM. Learn why the CDM is so important and see several actual CDMs and learn how each was built. This video was recorded live during Steve Hoberman's Data Modeling Master Class. More on this 3-day data modeling course at stevehoberman.com.
This book offers a comprehensive approach to the question of how artificial intelligence (AI) impacts politics, economy, and the society today. In this view, it is quintessential for understanding the complex nature of AI and its role in today’s world. The book has been divided into three parts. Part one is devoted to the question of how AI will be used for security and defense purposes, including combat in war zones. Part two looks at the value added of AI and machine learning for decision-making in the fields of politics and business. Part three consists of case studies—covering the EU, the USA, Saudi Arabia, Portugal, and Poland—that discuss how AI is being used in the realms of politics, security and defense. The discussion in the book opens with the question of the nature of AI, as well as of ethics and the use of AI in combat. Subsequently, the argument covers issues as diverse as the militarization of AI, the use of AI in strategic studies and military strategy design. These topics are followed by an insight into AI and strategic communication (StratCom), including disinformation, as well as into AI and finance. The case-studies included in part 3 of the book offer a captivating overview of how AI is being employed to stimulate growth and development, to promote data- and evidence-driven policy-making, to enable efficient and inclusive digital transformation and other related issues. Written by academics and practitioners in an academically sound, yet approachable manner, this volume queries issues and topics that form the thrust of processes that transform world politics, economics and society. As such, this volume will serve as the primer for students, researchers, lectures and other professionals who seek to understand and engage with the variety of issues AI implicates.
This book addresses the development and adoption of artificial intelligence (AI) in and by companies and the consequent need for private sector AI regulation. Highlighting the challenges to responsible business conduct and considering stakeholder interests, it identifies ethical concerns and discusses AI standards and AI norms. Based on this needs-based analysis, the author chooses relational economics as a suitable approach to develop a theoretical AI governance model. In doing so, AI is conceptualized within relational economics in the form of an autopoietic system. Building on this theoretical contribution, the book specifies the governance adaptivity of the relational AI governance approach for an unregulated AI market and for the case of the pending E.U. AI regulation, and complements it with inductively conducted categories that summarize the main research streams in AI ethics.