Introduction to Responsible AI

Implement Ethical AI Using Python

Häftad, Engelska, 2023

496 kr

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Beskrivning

Learn and implement responsible AI models using Python. This book will teach you how to balance ethical challenges with opportunities in artificial intelligence.The book starts with an introduction to the fundamentals of AI, with special emphasis given to the key principles of responsible AI. The authors then walk you through the critical issues of detecting and mitigating bias, making AI decisions understandable, preserving privacy, ensuring security, and designing robust models. Along the way, you’ll gain an overview of tools, techniques, and code examples to implement the key principles you learn in real-world scenarios.The book concludes with a chapter devoted to fostering a deeper understanding of responsible AI’s profound implications for the future. Each chapter offers a hands-on approach, enriched with practical insights and code snippets, enabling you to translate ethical considerations into actionable solutions. What You Will LearnUnderstand the principles of responsible AI and their importance in today's digital worldMaster techniques to detect and mitigate bias in AIExplore methods and tools for achieving transparency and explainabilityDiscover best practices for privacy preservation and security in AIGain insights into designing robust and reliable AI modelsWho This Book Is ForAI practitioners, data scientists, machine learning engineers, researchers, policymakers, and students interested in the ethical aspects of AI

Produktinformation

Utgivningsdatum:2023-11-23
Mått:155 x 235 x 15 mm
Vikt:310 g
Format:Häftad
Språk:Engelska
Antal sidor:184
Förlag:APress
ISBN:9781484299814

Utforska kategorier

Informationsteknik: allmänt inom Data och IT
Artificiell intelligens inom Data och IT
Programspråk inom Data och IT

Mer om författaren

Avinash Manure is a seasoned machine learning professional with more than ten years of experience in building, deploying, and maintaining state-of-the-art machine learning solutions across different industries. He has more than six years of experience in leading and mentoring high performance teams in developing ML systems catering to different business requirements. He is proficient in deploying complex machine learning and statistical modeling algorithms/ and techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.He is the author of Learn Tensorflow 2.0 and Introduction to Prescriptive AI, both with Apress.Avinash holds a bachelor’s degree in Electronics Engineering from Mumbai University and earned his Masters in Business Administration (Marketing) from the University of Pune. He resides in Bangalore with his wife and child. He enjoys travelling to new places and reading motivational books.Shaleen is a machine learning engineer with 4+ years of experience in building, deploying, and managing cutting-edge machine learning solutions across varied industries. He has developed several frameworks and platforms that have significantly streamlined processes and improved efficiency of machine learning teams. Shaleen Bengani has authored the book Operationalizing Machine Learning Pipelines as well as three research papers in the deep learning space.He holds a bachelors degree in Computer Science and Engineering from BITS Pilani, Dubai Campus, where he was awarded the Director’s Medal for outstanding all-around performance. In his leisure time, he likes playing table tennis and reading.Saravanan S is an AI engineer with more than six years of experience in data science and data engineering. He has developed robust data pipelines for developing and deploying advanced machine learning models, genratinginsightful reports, and ensuring cutting edge solutions for diverse industries.Saravanan earned a masters degree in statistics from Loyola College from Chennai. In his spare time he likes traveling, reading books and playing games.

Innehållsförteckning

Chapter 1: Introduction.- Chapter 2: Bias and Fairness.- Chapter 3: Transparency and Explainability.- Chapter 4: Privacy and Security.- Chapter 5: Ensuring Robustness and Reliability.- Chapter 6: Conclusion.