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.

This book argues that ethical evaluation of AI should be an integral part of public service ethics and that an effective normative framework is needed to provide ethical principles and evaluation for decision-making in the public sphere, at both local and international levels. It introduces how the tenets of prudential rationality ethics, through critical engagement with intersectionality, can contribute to a more successful negotiation of the challenges created by technological innovations in AI and afford a relational, interactive, flexible and fluid framework that meets the features of AI research projects, so that core public and individual values are still honoured in the face of technological development. This book will be of key interest to scholars, students, and professionals engaged in public management and ethics management, AI ethics, public organizations, public service leadership and more broadly to public administration and policy, as well as applied ethics and philosophy.

This interdisciplinary and international handbook captures and shapes much needed reflection on normative frameworks for the production, application, and use of artificial intelligence in all spheres of individual, commercial, social, and public life.

"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"--

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.