In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

AI has contributed a lot and biometrics is no exception. To make AI solutions commercialized/fully functional, one requires trustworthy and explainable AI (XAI) solutions while respecting ethical issues. Within the scope of biometrics, the book aims at both revisiting ethical AI principles by taking into account state-of-the-art AI-guided tools and their responsibilities i.e., responsible AI. With this, the long-term goal is to connect with how we can enhance research communities that effectively integrate computational expertise (with both explainability and ethical issues). It helps combat complex and elusive global security challenges that address our national concern in understanding and disrupting the illicit economy.

The impact of artificial intelligence (AI) on business and society has been significant, with the incorporation of AI technologies such as robots, facial recognition, algorithms, and natural language processing into business leading to both corporate benefits and potential challenges for stakeholders. The question of how to engage in responsible business practices in the era of AI is an important one, and there is a need for more research on the relationship between AI and corporate social responsibility (CSR). As AI becomes more prevalent, there is a growing focus on the ethical implications of AI and the potential for AI to perpetuate biases or to displace human workers. CSR initiatives can include considerations of ethical AI in the development and use of AI systems. AI has the potential to solve many global challenges and improve people's lives, but it can also have negative consequences if not developed and used responsibly. CSR initiatives can focus on the social impact of AI, including efforts to ensure that the benefits of AI are distributed fairly and that AI is used for the common good. CSR initiatives often involve engaging with stakeholders, including employees, customers, and communities, to understand their needs and concerns and to ensure that their interests are taken into account. This can include engaging with stakeholders about the use of AI in the organization and its potential impacts The adoption of AI in business is changing many aspects of doing business in a socially responsible manner, and there is a need to examine the potential unethical behaviors and novel ways of engaging in CSR that may arise. This book aims to focus on AI and CSR, and to advance our understanding of the role of AI in organizations and the literature on CSR by assembling high-quality papers with a strong connection between theory and practice.

This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it.

The digital world is characterized by its immediacy, its density of information and its omnipresence, in contrast to the concrete world. Significant changes will occur in our society as AI becomes integrated into many aspects of our lives. This book focuses on this vision of universalization by dealing with the development and framework of AI applicable to all. It develops a moral framework based on a neo-Darwinian approach - the concept of Ethics by Evolution - to accompany AI by observing a certain number of requirements, recommendations and rules at each stage of design, implementation and use. The societal responsibility of artificial intelligence is an essential step towards ethical, eco-responsible and trustworthy AI, aiming to protect and serve people and the common good in a beneficial way.

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. In response, researchers and organizations have been working to publish principles and develop AI regulations for the responsible use of AI in consequential application domains. However, these theoretically formulated principles and regulations also need to be turned into actionable algorithms to materialize AI for good.This book introduces a unified perspective of Socially Responsible AI to help bridge conceptual AI principles to responsible AI practice. It begins with an interdisciplinary definition of socially responsible AI and the AI responsibility pyramid. Existing efforts seeking to materialize the mainstream responsible AI principles are then presented. The book also discusses how to leverage advanced AI techniques to address the challenging societal issues through Protecting, Informing, and Preventing, and concludes with open problems and challenges.This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges of socially responsible AI, and to identify how their areas of expertise can contribute to making AI socially responsible.

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.

As smart cities become more prevalent, the need for explainable AI (XAI) applications has become increasingly important. Advances in Explainable AI Applications for Smart Cities is a co-edited book that showcases the latest research and development in XAI for smart city applications. This book covers a wide range of topics, including medical diagnosis, finance and banking, judicial systems, military training, manufacturing industries, autonomous vehicles, insurance claim management, and cybersecurity solutions. Through its diverse case studies and research, this book provides valuable insights into the importance of XAI in smart city applications. This book is an essential resource for undergraduate and postgraduate students, researchers, academicians, industry professionals, and scientists working in research laboratories. It provides a comprehensive overview of XAI concepts, advantages over AI, and its applications in smart city development. By showcasing the impact of XAI on various smart city applications, the book enables readers to understand the importance of XAI in creating more sustainable and efficient smart cities. Additionally, the book addresses the open challenges and research issues related to XAI in modern smart cities, providing a roadmap for future research in this field. Overall, this book is a valuable resource for anyone interested in understanding the importance of XAI in smart city applications.