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

Responsible AI in the Enterprise is a comprehensive guide to ethical, transparent, and compliant AI systems, covering key concepts, tools, and techniques for creating fair, robust accountable machine learning models.

Key Features

• Learn Ethical AI Principles, Frameworks, & Governance
• Understand the concepts of Fairness assessment & bias mitigation
• Get ot grips with Explainable AI & transparency

Book Description

Responsible AI in the Enterprise offers a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts like explainable, safe, ethical, robust, transparent, auditable, and interpretable machine learning models, this book equips developers with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Readers will gain an in-depth understanding of FairLearn and InterpretML, as well as other tools like Google's What-If Tool, ML Fairness Gym, IBM's AI 360 Fairness tool, Aequitas, and FairLearn. The book covers various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance standards recommendations. It provides practical insights on how to use AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Readers will explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, and learn how to use FairLearn for fairness assessment and bias mitigation. By the end of this book you will ge to grips with tools and techniques available to create transparent and accountable machine learning models.

What you will learn

• Understand the importance of ethical considerations in AI and recognize the significance of model governance standards in ensuring responsible AI practices.
• Detect and mitigate biases in data and algorithms, and appreciate the need for fairness in AI decision-making.
• Recognize the importance of accountability regulations in promoting ethical AI, and understand the impact of AI on society.
• Analyze model interpretability methods and tools and apply them to understand AI models' decision-making processes.
• Evaluate AI compliance standards and identify their role in ensuring trustworthy AI.
• Utilize AI governance frameworks to develop a comprehensive approach to implementing responsible AI practices.
• Utilize cloud AI explainability toolkits to build transparency and accountability in AI models.
• Understand the principles of responsible AI in AWS, GCP, and Azure, and recognize their role in promoting ethical AI practices.

Who This Book Is For

This book is essential 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.

Table of Contents

1. A Primer on Explainable and Ethical AI
2. Algorithms Gone Wild - Bias's Greatest Hits
3. Opening the Algorithmic Blackbox
4. Operationalizing Model Monitoring
5. Model Governance - Audit, and Compliance Standards & Recommendations
6. Enterprise Starter Kit for Fairness, Accountability and Transparency
7. Interpretability Toolkits and Fairness Measures - AWS, GCP, Azure, and AIF 360
8. Fairness in AI System with Microsoft FairLearn
9. Fairness assessment and bias mitigationFairLearn and Responsible AI Toolbox
10. Foundation Models, LLMs, and Azure Open AI: Navigating the Landscape of Responsible AI