ISBN: 9781484293058 - building responsible ai algorithms: a framework for transparency, fairness, safety, privacy, and robustness (25 resultados)

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Paperback. Condición: new. Paperback. This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts that in some cases have caused loss of life and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will LearnBuild AI/ML models using Responsible AI frameworks and processesDocument information on your datasets and improve data qualityMeasure fairness metrics in ML modelsIdentify harms and risks per task and run safety evaluations on ML modelsCreate transparent AI/ML modelsDevelop Responsible AI principles and organizational guidelinesWho This Book Is ForAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms Intermediate-Advanced user level Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. 2023. paperback. . . . . .

Paperback. Condición: Brand New. 207 pages. 9.25x6.10x0.44 inches. In Stock.

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Condición: Wie Neu. Zustandsbeschreibung: minimale Lagerspuren/minor shelfwear. A Framework for Transparency, Fairness, Safety, Privacy, and Robustness. XVII,190 Seiten mit 5 Abb., broschiert (Apress 2023). Statt EUR 35,30. Gewicht: 325 g - Softcover/Taschenbuch.

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Paperback. Condición: new. Paperback. This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts that in some cases have caused loss of life and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers. What You Will LearnBuild AI/ML models using Responsible AI frameworks and processesDocument information on your datasets and improve data qualityMeasure fairness metrics in ML modelsIdentify harms and risks per task and run safety evaluations on ML modelsCreate transparent AI/ML modelsDevelop Responsible AI principles and organizational guidelinesWho This Book Is ForAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms Intermediate-Advanced user level Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. Building Responsible AI Algorithms | A Framework for Transparency, Fairness, Safety, Privacy, and Robustness | Toju Duke | Taschenbuch | xvii | Englisch | 2023 | Apress | EAN 9781484293058 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: gut. Building Responsible AI Algorithms In englischer Sprache. pages.

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Paperback. Condición: Brand New. 207 pages. 9.25x6.10x0.44 inches. In Stock. This item is printed on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts - that in some cases have caused loss of life - and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.What You Will LearnBuild AI/ML models using Responsible AI frameworks and processesDocument information on your datasets and improve data qualityMeasure fairness metrics in ML modelsIdentify harms and risks per task and run safety evaluations on ML modelsCreate transparent AI/ML modelsDevelop Responsible AI principles and organizational guidelinesWho This Book Is ForAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms 208 pp. Englisch.…

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts - that in some cases have caused loss of life - and develop models that are fair, transparent, safe, secure, and robust.The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.What You Will LearnBuild AI/ML models using Responsible AI frameworks and processesDocument information on your datasets and improve data qualityMeasure fairness metrics in ML modelsIdentify harms and risks per task and run safety evaluations on ML modelsCreate transparent AI/ML modelsDevelop Responsible AI principles and organizational guidelinesWho This Book Is ForAI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms.…

Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced user level|Builds your awareness of the potential risks and harms that AI algorithms poseCovers the issues that must be addressed by AI practitioners in relation to responsibility and ethicsPresents a framework for imp.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts - that in some cases have caused loss of life - and develop models that are fair, transparent, safe, secure, and robust.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.…