Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Learn ethical AI principles, frameworks, and governance
• Understand the concepts of fairness assessment and bias mitigation
• Introduce explainable AI and transparency in your machine learning models

Book Description:

Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance.

Throughout the book, you'll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You'll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You'll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you'll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You'll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.

By the end of this book, you'll be well-equipped with tools and techniques to create transparent and accountable machine learning models.

What You Will Learn:

• Understand explainable AI fundamentals, underlying methods, and techniques
• Explore model governance, including building explainable, auditable, and interpretable machine learning models
• Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction
• Build explainable models with global and local feature summary, and influence functions in practice
• Design and build explainable machine learning pipelines with transparency
• Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms

Who this book is for:

This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.

Formato	Tapa suave
Número de Páginas	318
Lenguaje	Inglés
Editorial	Packt Publishing
Fecha de Publicación	2023-07-31
Dimensiones	9.25" x 7.5" x 0.67" pulgadas
Letra Grande	No
Con Ilustraciones	No

Dawe, Heather

Heather Dawe, MSc. is a renowned data and AI thought leader with over 25 years of experience in the field. Heather has innovated with data and AI throughout her career, highlights include developing the first data science team in the UK public sector and leading on the development of early machine learning and AI assurance processes for the National Health Service (NHS) in England. Heather currently works with large UK Enterprises, innovating with data and technology to improve services in the health, local government, retail, manufacturing, and finance sectors. A STEM Ambassador and multidisciplinary data science pioneer, Heather also enjoys mountain running, rock climbing, painting, and writing. She served as a jury member for the 2021 Banff Mountain Book Competition and guest edited the 2022 edition of The Himalayan Journal. Heather is the author of several books inspired by mountains and has written for national and international print publications including The Guardian and Alpinist.

Masood, Adnan

Adnan Masood, PhD is an artificial intelligence and machine learning researcher, visiting scholar at Stanford AI Lab, software engineer, Microsoft MVP (Most Valuable Professional), and Microsoft's regional director for artificial intelligence. As chief architect of AI and machine learning at UST Global, he collaborates with Stanford AI Lab and MIT CSAIL, and leads a team of data scientists and engineers building artificial intelligence solutions to produce business value and insights that affect a range of businesses, products, and initiatives.