Responsible AI in the Enterprise

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.

Type
ebook
Category
Data
publication date
2023-07-31
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

no of pages
318
duration
636
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
approach
Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, you will begin by exploring state of model governance, the algorithms & techniques, pitfalls, and then explore the relevant technologies from cloud service providers and finally doing a deep dive with Microsoft FairLearn.
audience
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.
meta description
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
short description
With Responsible AI in the Enterprise, you’ll delve into explainable AI, robust ML monitoring, and model governance, while exploring fairness and interpretability toolkits from major cloud providers. You’ll master bias mitigation techniques and be ready to apply them in real-world scenarios with FairLearn and Responsible AI Toolbox.
subtitle
Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI
keywords
AWS, Machine learning book, Ai books 2023, Ai books bestsellers, Ai books best sellers 2023, Ai ethics book
Product ISBN
9781803230528