Details

AI for the Good


AI for the Good

Artificial Intelligence and Ethics
Management for Professionals

von: Stefan H. Vieweg

90,94 €

Verlag: Springer
Format: PDF
Veröffentl.: 24.04.2021
ISBN/EAN: 9783030669133
Sprache: englisch

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

While technology advances at a high pace in the age of machine learning, there is a lack of clear intent and framing of acceptable ethical standards. This book brings together the complex topic of "good" technology in a cross-functional way, alternating between theory and practice.<div>The authors address the ever-expanding discussion on Artificial Intelligence (AI) and ethics by providing an orientation. Pragmatic and recent issues are especially taken into account such as the collateral effects of the COVID19 pandemic. An up-to-date overview of digitization - already a very broad field in itself - is presented along with an analysis of the approaches of AI from an ethical perspective. Furthermore, concrete approaches to consider appropriate ethical principles in AI-based solutions are offered.&nbsp;</div><div>The book will be appealing to academics, from humanities or business or technical disciplines, as well as practitioners who are looking for an introduction to the topic and an orientation with concrete questions and assistance.</div>
Ethical Concepts and Business Virtues:&nbsp;Ethics for Non-philosophers.&nbsp;Basis of Ethics and Ethical Perspectives.-&nbsp;Human Rationality and Morale.-&nbsp;&nbsp;Business Ethics.-&nbsp;Ethics in an International Context.-&nbsp;Value-based Corporate Management and Integral Intelligence.-&nbsp;AI and the Digital Age:&nbsp;Digitization: Learnings from Ancient Disruptions, AI and the Digital Trio’s Functional Stage and AI-Superpowers Disrupting Us.-&nbsp;AI and the Ethical Challenge.-&nbsp;&nbsp;Practical Examples:&nbsp;AI in the Financial Industries. Between Apathy and Hysteria.-&nbsp;Ethical Best Practice Applying AI in a Socially Sensible Manner.-&nbsp;Yes, AI Can. The Artificial Intelligence Gold Rush&nbsp;Between Optimistic HR Software Providers, Skeptical HR Managers, and Corporate Ethical Virtues.-&nbsp;Ethical AI Implementation.
Stefan Vieweg is Director of the Institute for Compliance and Corporate Governance at the Rheinische Fachhochschule University of Applied Sciences Cologne (Germany). He has over 20 years of international management experience including executive and supervisory board positions, particularly in the ICT sector. He is qualified as a Chartered Financial Analyst (CFA), certified Systemic Change Manager, and SAFe® SPC-RTE. His application-oriented research and consultancy focuses on agile and resilient corporate management, AI-based corporate governance, and CSR. He has more than 100 publications in the areas of agility, business transformation, compliance, controlling, digitalization, leadership, and sustainability.<br>
While technology advances at a high pace in the age of machine learning, there is a lack of clear intent and framing of acceptable ethical standards. This book brings together the complex topic of "good" technology in a cross-functional way, alternating between theory and practice.<div>The authors address the ever-expanding discussion on Artificial Intelligence (AI) and ethics by providing an orientation. Pragmatic and recent issues are especially taken into account such as the collateral effects of the COVID19 pandemic. An up-to-date overview of digitization - already a very broad field in itself - is presented along with an analysis of the approaches of AI from an ethical perspective. Furthermore, concrete approaches to consider appropriate ethical principles in AI-based solutions are offered.&nbsp;</div><div>The book will be appealing to academics, from humanities or business or technical disciplines, as well as practitioners who are looking for an introduction to the topic and an orientation with concrete questions and assistance.</div><div><br></div>
Provides an ethical compass to navigate through the decision-making process Illustrates ethical issues in applying machine learning technologies and their solutions Provides an overview on digitization and technology approaches including their limitations

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