Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

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.