ISBN: 9798868811654 - responsible ai in practice: a practical guide to safe and human ai (14 risultati)

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Paperback. Condizione: New. This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled "Safe Human-centered AI (SAFE-HAI)," which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks.  This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will LearnKnow the key principles behind Responsible AI and associated risksBecome familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risksBe aware of the fundamentals of AI regulations and policies and how to adopt themUnderstand AI governance basics and implementation guidelines Who This Book Is ForAI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.…

Condizione: As New. Unread book in perfect condition.

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Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Paperback. Condizione: New. This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled "Safe Human-centered AI (SAFE-HAI)," which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks.  This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will LearnKnow the key principles behind Responsible AI and associated risksBecome familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risksBe aware of the fundamentals of AI regulations and policies and how to adopt themUnderstand AI governance basics and implementation guidelines Who This Book Is ForAI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.…

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.The centerpiece of this book is a risk management and assessment framework titled 'Safe Human-centered AI (SAFE-HAI),' which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks.This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission).What You Will LearnKnow the key principles behind Responsible AI and associated risksBecome familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risksBe aware of the fundamentals of AI regulations and policies and how to adopt themUnderstand AI governance basics and implementation guidelinesWho This Book Is ForAI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities 224 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.The centerpiece of this book is a risk management and assessment framework titled 'Safe Human-centered AI (SAFE-HAI),' which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks.This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission).What You Will LearnKnow the key principles behind Responsible AI and associated risksBecome familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risksBe aware of the fundamentals of AI regulations and policies and how to adopt themUnderstand AI governance basics and implementation guidelinesWho This Book Is ForAI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities.The centerpiece of this book is a risk management and assessment framework titled 'Safe Human-centered AI (SAFE-HAI),' which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks.This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission).What You Will LearnKnow the key principles behind Responsible AI and associated risksBecome familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risksBe aware of the fundamentals of AI regulations and policies and how to adopt themUnderstand AI governance basics and implementation guidelinesWho This Book Is ForAI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universitiesSpringer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.…