This book takes a historical approach to explore data, algorithms, their use in practice through applications of AI in various settings, and all of the surrounding ethical and DEI implications. Summarizing our current knowledge and highlighting gaps, it offers original examples from empirical research in various settings, such as healthcare, social media, and the GIG economy. The author investigates how systems relying on a binary structure (machines) work in systems that are instead analogic (societies). Further, he examines how underrepresented populations, who have been historically penalized by technologies, can play an active role in the design of automated systems, with a specific focus on the US legal and social system. One issue is that main tasks of machines concern classification, which, while efficient for speeding up decision-making processes, are inherently biased. Ultimately, this work advocates for ethical design and responsible implementation and deployment of technology in organizations and society through through government-sponsored social justice, in contrast with free market policies. This interdisciplinary text contributes to the timely and relevant debate on algorithmic fairness, biases, and potential discriminations. It will appeal to researchers in business ethics and information systems while building on theories from anthropology, psychology, sociology, management, marketing, and economics. Marco Marabelli is a Professor of Computer Information Systems at Bentley University, USA. His research focuses on the ethical and DEI implications of the use of emerging technologies in organizations and society and on the historical and legal aspects concerning social injustice associated with the use of artificial intelligence.

How did racism creep into the algorithms that govern our daily lives, from banking and shopping, to job applications? Connecting the legacy of enlightenment racism to forms of discrimination in modern day algorithms and Artificial Intelligence, this volume examines what data feeds into AI technology - and how this data will shape the future of humanity. Delving into the narratives enveloping the development of AI systems, with a particular emphasis on "tech-giants" and the ideas of Mark Zuckerberg, Elon Musk and Bill Gates, Arshin Adib-Moghaddam explains how and why technology aids and abets various forms of extremism, entrenches social hierarchies and discriminatory boundaries and how this will impact international security and human rights in the future.

What will you do when your AI misbehaves? The promise of artificial intelligence is automated decision-making at scale, but that means AI also automates risk at scale. Are you prepared for that risk? Already, many companies have suffered real damage when their algorithms led to discriminatory, privacy-invading, and even deadly outcomes. Self-driving cars have hit pedestrians; HR algorithms have precluded women from job searches; mortgage systems have denied loans to qualified minorities. And often the companies who deployed the AI couldn't explain why the black box made the decision it did. In this environment, AI ethics isn't merely an academic curiosity, it's a business necessity. In Ethical Machines, Reid Blackman gives you all you need to understand AI ethics as a risk management challenge. He'll help you build, procure, and deploy AI in a way that's not only ethical but also safe in terms of your organization's reputation, regulatory compliance, and legal standing—and do it at scale. And don't worry—the book's purpose is to get work done, not to ponder deep and existential questions about ethics and technology. Blackman's clear and accessible writing helps make a complex and often misunderstood concept like ethics easy to grasp. Most importantly, Blackman makes ethics actionable by tackling the big three ethical risks with AI—bias, explainability, and privacy—and tells you what to do (and what not to do) to mitigate them. With practical approaches to everything from writing a strong statement of AI ethics principles to creating teams that effectively evaluate ethical risks, Ethical Machines is the one guide you need to ensure your AI advances your company's objectives instead of undermining them.

Business approaches in today’s society have become technologically-driven and highly-applicable within various professional fields. These business practices have transcended traditional boundaries with the implementation of internet technology, making it challenging for professionals outside of the business world to understand these advancements. Interdisciplinary research on business technology is required to better comprehend its innovations. Interdisciplinary Approaches to Digital Transformation and Innovation provides emerging research exploring the complex interconnections of technological business practices within society. This book will explore the practical and theoretical aspects of e-business technology within the fields of engineering, health, and social sciences. Featuring coverage on a broad range of topics such as data monetization, mobile commerce, and digital marketing, this book is ideally designed for researchers, managers, students, engineers, computer scientists, economists, technology designers, information specialists, and administrators seeking current research on the application of e-business technologies within multiple fields.

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."

Organizations face a pressing challenge in today's rapidly evolving economies: navigating the ethical complexities of adopting Artificial Intelligence (AI) and related technologies. As AI becomes increasingly integral to operations, transparency, fairness, accountability, and privacy concerns are more critical than ever. Organizations need practical guidance to develop and implement AI ethics strategies effectively. Cases on AI Ethics in Business offers a comprehensive solution by examining AI Ethics through theoretical lenses and innovative practices. It provides a roadmap for organizations to address ethical challenges in AI adoption, offering insights from leaders in the field. With a focus on theory-to-practice, the book equips readers with actionable strategies and frameworks to navigate the ethical implications of AI, ensuring responsible and sustainable AI deployment.

This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems? This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place.

An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI.

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. The authors interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them.