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.

Provides models for how organizations can achieve responsible and ethical AI Offers a comprehensive account of how leading companies govern their use of AI Is one of the first works to focus specifically on AI ethics management rather than AI ethics principles or tools This book is open access, which means that you have free and unlimited access

Autorentext
Dennis Hirsch is Professor of Law and of Computer Science at The Ohio State University where he also serves as Director of the Program on Data and Governance and as a core faculty member of the Translational Data Analytics Institute (TDAI). His research and teaching, and the program that he directs, focus on the law, policy, ethics, and management of advanced analytics and AI. The author of numerous articles and an award-winning book, Professor Hirsch is the co-editor of the SSRN eJournal on Artificial Intelligence - Law, Policy, and Ethics, the founding Chair of TDAI's Responsible Data Science Community of Practice, the founder of the Ohio Data Ethics Working Group, and a member of the Advisory Board for the International Association of Privacy Professionals' (IAPP) AI Governance Center. In 2010, he served as a Fulbright Senior Professor at the University of Amsterdam where he studied Dutch privacy regulation. He received his J.D. from Yale Law School. Tim Bartleyis a Professor in the Earth Commons Institute and Department of Sociology at Georgetown University. He previously held positions at Washington University in St. Louis, Stockholm University, Ohio State University, and Indiana University. He is an organizational, political, and economic sociologist, whose research focuses largely on private forms of regulation and questions of fairness and sustainability in global industries. His previous work includes the book Rules without Rights: Land, Labor, and Private Authority in the Global Economy (Oxford University Press, 2018) and articles such as "Global Markets, Corporate Assurances, and the Legitimacy of State Intervention" (American Sociological Review, 2022), "Transnational Corporations and Global Governance" (Annual Review of Sociology, 2018), "Shaming the Corporation" (American Sociological Review, 2014), and "The Digital Surveillance Society" (Contemporary Sociology, 2019). He received his PhD from the University of Arizona and hasbeen lucky to spend time as a visiting scholar at the University of Konstanz, University of St. Gallen, Max Planck Institute for the Study of Societies, MIT, Princeton, and Sun Yat-sen University. Aravind Chandrasekaran (AC) is the Fisher Distinguished Professor in Operations and Business Analytics at the Fisher College of Business, The Ohio State University. He received his PhD in Operations and Management Sciences from the University of Minnesota. Dr. Chandrasekaran's research investigates innovation, learning and knowledge creation issues in a variety of industries including high-tech R&D, manufacturing and health-care. His research has been published in all top OM journals. Dr. Chandrasekaran currently serves as the Associate Dean for Graduate Programs and Executive Education at the Fisher College of Business. He oversees all the graduate programs including Full-time MBA, Working Professional MBA, Specialized Master's as well as executive master's programs including EMBA, the Master of Business in Operational Excellence (MBOE) and non-degree executive education. He has developed several custom teaching and research programs for organizations such as Tata Consultancy Services (TCS), Ford Motor Company and Zimmer. He has won several teaching awards including the 2016 & 2012 Best Outstanding Core Professor Award (WPMBA), as well as the 2013 Pace Setter Award for Teaching Excellence. Davon Norris, Assistant Professor of Organizational Studies at the University of Michigan, is an economic sociologist who tries to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. Generally, this empirically manifests in work that studies credit, debt, and finance. However, he more specifically investigates the functioning and consequences of a range of scores or ratings, from state and municipal government credit ratings to algorithmic consumer credit scores.

Inhalt

1. What is AI Ethics Management and Why Does it Matter?.- 2. AI Can Injure People and Damage Business Reputation.- 3. Why Companies Pursue AI Ethics Management.- 4. How to Draw Substantive Lines Between Ethical, and Unethical, Uses of AI.- 5. Management Structures and Processes for Achieving Responsible and Ethical AI.- 6. The Next Stage: AI for the Social Good.- 7. Conclusion.