From Trustworthy Ai Principles To Public Procurement Practices

This book is an early warning to public officials, policymakers, and procurement practitioners on the impact of AI on the public sector. Many governments have established national AI strategies and set ambitious goals to incorporate AI into the public infrastructure, while lacking AI-specific procurement guidelines. AI is not traditional software, and traditional processes are not sufficient to meet the challenges AI brings. Today's decisions to embed AI and algorithmic systems into public system infrastructure can - and will - have serious repercussions in the future. The promise of AI systems is to make the public sector more efficient, effective, fair, and sustainable. However, AI systems also bring new and emerging risks which can impact rights and freedoms. Therefore, guardrails are necessary to consider the socio-technical dimensions and impact on individuals, communities, and society at large. It is crucial that public sector decision-makers understand the emerging risks of AI systems, the impact on the agency and the wider public infrastructure, and have the means to independently validate vendor claims. This book is a result of interviews with more than 20 public procurement professionals across countries, offering an in-depth analysis of the risks, incidents, governance practices, and emerging good practices around the world, and provides valuable procurement policy and process recommendations to address and mitigate these risks.

This book is an early warning to public officials, policymakers, and procurement practitioners on the impact of AI on the public sector. Many governments have established national AI strategies and set ambitious goals to incorporate AI into the public infrastructure, while lacking AI-specific procurement guidelines. AI is not traditional software, and traditional processes are not sufficient to meet the challenges AI brings. Today’s decisions to embed AI and algorithmic systems into public system infrastructure can – and will – have serious repercussions in the future. The promise of AI systems is to make the public sector more efficient, effective, fair, and sustainable. However, AI systems also bring new and emerging risks which can impact rights and freedoms. Therefore, guardrails are necessary to consider the socio-technical dimensions and impact on individuals, communities, and society at large. It is crucial that public sector decision-makers understand the emerging risks of AI systems, the impact on the agency and the wider public infrastructure, and have the means to independently validate vendor claims. This book is a result of interviews with more than 20 public procurement professionals across countries, offering an in-depth analysis of the risks, incidents, governance practices, and emerging good practices around the world, and provides valuable procurement policy and process recommendations to address and mitigate these risks.

THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI ̃FROM MUL TI°LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES. AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI. First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle. Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques. Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering. Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry. Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors. Real world case studies to demonstrate responsible AI in practice. Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide.

This comprehensive book covers the concept of trustworthy AI, which is an essential paradigm in today's AI-based system. It is designed for developers, researchers, and end-users seeking a deep understanding of how AI systems are ethical, reliable, and equitable. The book begins by addressing the principles of trustworthy AI and their importance in high-risk applications like healthcare, finance, autonomous vehicles, and law. The core of this book is an exploration of the six principles of trustworthy AI, i.e. reliability, transparency, fairness, accountability, inclusiveness, privacy, and security. Each principle is thoroughly examined, demonstrating how they interact with AI systems to make them more efficient, and robust but also ethical, and equitable. This book explores the concept of transparency in depth, highlighting the necessity for AI systems to be understandable and accessible to all stakeholders. Fairness is examined as a critical aspect, focusing on designing and deploying AI systems that mitigate biases and offer equitable outcomes for all. It also covers AI systems' accountability, inclusiveness, privacy, and security. It is a big challenge to develop AI technologies that are advanced in capabilities and uphold the highest ethical standards, building trust among users and stakeholders. This book is written for anyone involved in AI development, deployment, and testing. It provides theoretical knowledge along with practical guidance. It serves as a comprehensive guide to transforming AI from a powerful tool into a force for good, aligning with human values and principles of fairness and transparency. As a result, readers will gain the knowledge and skill to create effective strategies for trustworthy AI, which is not just technologically advanced but also ethically and 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.

This book explores the ethical implications of the burgeoning adoption and deployment of Autonomous Decision Making and Algorithmic Learning Systems (ADM/ALS) on human rights and societal values as well as these systems’ potential social harms and benefits. After two millennia of recorded civilization, consideration of ethics and social values in all that we strive for is a long-overdue phenomenon. Therefore this is a journey that we’ve just embarked on thanks to the emergence of ADM/ALS and should not be treated as a destination in line with many other facets and emergent properties of products, services, and systems. This book informs policymakers and practitioners about best practices in technology ethics pertinent to many disciplines and sectors.

These principles of corporate governance, endorsed by the OECD Council at Ministerial level in 1999, provide guidelines and standards to insure inclusion, accountability and abilit to attract capital.

On the 17 of July 2020, the High-Level Expert Group on Artificial Intelligence (AI HLEG) presented their final Assessment List for Trustworthy Artificial Intelligence. Following a piloting process where over 350 stakeholders participated, an earlier prototype of the list was revised and translated into a tool to support AI developers and deployers in developing Trustworthy AI. The tool supports the actionability the key requirements outlined by the Ethics Guidelines for Trustworthy Artificial Intelligence (AI), presented by the High-Level Expert Group on AI (AI HLEG) presented to the European Commission, in April 2019. The Ethics Guidelines introduced the concept of Trustworthy AI, based on seven key requirements: human agency and oversight technical robustness and safety privacy and data governance transparency diversity, non-discrimination and fairness environmental and societal well-being and accountability Through the Assessment List for Trustworthy AI (ALTAI), AI principles are translated into an accessible and dynamic checklist that guides developers and deployers of AI in implementing such principles in practice. ALTAI will help to ensure that users benefit from AI without being exposed to unnecessary risks by indicating a set of concrete steps for self-assessment. Download the Assessment List for Trustworthy Artificial Intelligence (ALTAI) (.pdf) The ALTAI is also available in a web-based tool version. More on the ALTAI web-based tool: https://futurium.ec.europa.eu/en/european-ai-alliance/pages/altai-assessment-list-trustworthy-artificial-intelligence

The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.

The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.