Responsible AI Made Easy with TensorFlow: The Ultimate Roadmap to Ethical AI: A Practical Guide to AI Fairness, Accountability, and Transparency

Deepen your understanding of responsible AI principles like fairness, accountability, and transparency. Gain experience with best practices and cutting-edge techniques using the TensorFlow toolkit, complete with code examples. Key Features Learn the principles of responsible AI and how to apply them in real-world scenarios Build fair and transparent ML models with TensorFlow, leveraging practical code examples Unlock deeper insights for AI governance and deployment by considering societal impacts Book DescriptionLooking to build machine learning models that are both accurate and fair? Look no further than “Responsible AI Made Easy with TensorFlow”! This hands-on guide will show you how to use TensorFlow, the popular open-source ML platform, to create AI-enabled products that prioritize fairness, accountability, and transparency. Using real-world case studies and practical code examples, you will learn the principles of responsible AI and how to apply them in your projects. You will take a step-by-step approach through the ML development workflow, with practical guidance on how you can make responsible choices at every stage. Further, you will gain expertise in cutting-edge techniques for preprocessing data and optimizing models for fair and equitable outcomes. This book also discusses broader issues at the intersection of AI and society. It explores critical socio-technical topics including governance, accountability, problem understanding, human factors, deployment, and monitoring of ML models. By the end of this book, with clear explanations, engaging examples, and practical advice, you will be able to responsibly build and deploy ML models into society - all while having fun along the way! What you will learn Gain a deep understanding of responsible AI principles in practice Get practical TensorFlow experience for responsible ML model creation Learn how to make responsible choices through real-world case studies Grow your expertise in evaluation and remediation of unfair bias Engage with AI + Society topics like governance and ethics with care Learn vital techniques for sustainable ML model deployment Who this book is forGet ready to level up your AI game! Whether you’re a seasoned ML practitioner or just starting out, “Responsible AI Made Easy with TensorFlow” has something for you. Perfect for ML researchers, data scientists, software engineers, and product managers alike, this book will help you create fair ML models and products that are beneficial to society. You’ll gain a deep understanding of responsible AI principles and how to apply them in practice using the TensorFlow toolkit. With case studies and code examples, this book is best suited for readers with a basic knowledge of ML and Python

Table of Contents:
Table of Contents What is Responsible AI? Fairness & Privacy Privacy` Robustness & Explainability An end-to-end Responsible AI pipeline Why data choices matter Evaluating ML datasets Remediating ML datasets Why model choices matter Evaluating ML models Remediating ML models AI Governance Deployment and Monitoring Humans-in-the-Loop Wrapping Up

About the Author :
Emmanuel Klu is a software engineer with over a decade of experience in data, reliability, and machine learning. He currently works at Google Research, using a data-centric and systems-thinking lens to explore responsible AI topics like fairness, bias and safety. Emmanuel studied Computer Science and Psychology at the Illinois Institute of Technology in Chicago. Sameer Sethi has spent more than 10 years developing products and platforms on network design, data warehousing and machine learning. Currently at Google Research, he focuses on building fair, equitable, and safe machine learning-driven solutions using participatory approaches. Sameer holds a Bachelor of Engineering in Information and Communications from Dublin City University, along with a Master of Technology in Communications from ITM University.