Building Responsible AI Algorithms - Toju Duke

Building Responsible AI Algorithms (eBook)

A Framework for Transparency, Fairness, Safety, Privacy, and Robustness

(Autor)

eBook Download: PDF
2023 | First Edition
XVII, 190 Seiten
Apress (Verlag)
978-1-4842-9306-5 (ISBN)
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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 Learn
  • Build AI/ML models using Responsible AI frameworks and processes
  • Document information on your datasets and improve data quality
  • Measure fairness metrics in ML models
  • Identify harms and risks per task and run safety evaluations on ML models
  • Create transparent AI/ML models
  • Develop Responsible AI principles and organizational guidelines


Who This Book Is For

AI 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


?Toju Duke is a Responsible AI Program Manager at Google with over 17 years of experience spanning across advertising, retail, not-for-profits, and tech industries. She designs Responsible AI programs focused on the development and implementation of Responsible AI frameworks, processes, and tools across Google's product and research teams. Toju is also the Founder of Diverse in AI, a community interest organization with a mission to provide inclusive and diverse AI through humanity. She provides consultation and advice on Responsible AI practices to organizations worldwide.



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
Erscheint lt. Verlag 31.8.2023
Zusatzinfo XVII, 190 p. 5 illus., 1 illus. in color.
Sprache englisch
Themenwelt Geisteswissenschaften Philosophie Ethik
Informatik Theorie / Studium Algorithmen
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte AI and ML Ethics • AI Transparency • Algorithmic fairness • Data Quality • explainability • Fairness Metrics • Humans in the Loop • Interpretability • Machine Learning Fairness • Machine Learning Safety • responsible AI • Responsible AI Frameworks • Transparent AI Models
ISBN-10 1-4842-9306-1 / 1484293061
ISBN-13 978-1-4842-9306-5 / 9781484293065
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