Responsible AI in the Enterprise
Packt Publishing Limited (Verlag)
978-1-80323-052-8 (ISBN)
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 DescriptionResponsible 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 forThis 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.
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. 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. Ed Price is a Senior Program Manager in Engineering at Microsoft, with an MBA in technology management. He leads Microsoft's efforts to publish Reference Architectures on the Azure Architecture Center. Previously, he drove datacenter deployment and customer feedback, and he ran Microsoft's customer feedback programs for Azure development, Service Fabric, IoT, Functions, and Visual Studio. He was also a technical writer at Microsoft for 6 years and helped lead TechNet Wiki. He is the co-author of five books, including Learn to Program with Small Basic and ASP.NET Core 5 for Beginners from Packt.
Table of Contents
A Primer on Explainable and Ethical AI
Algorithms Gone Wild - Bias's Greatest Hits
Opening the Algorithmic Blackbox
Operationalizing Model Monitoring
Model Governance - Audit, and Compliance Standards & Recommendations
Enterprise Starter Kit for Fairness, Accountability and Transparency
Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360
Fairness in AI System with Microsoft FairLearn
Fairness Assessment and Bias Mitigation with FairLearn and Responsible AI Toolbox
Foundational Models and Azure OpenAI
| Erscheinungsdatum | 03.08.2023 |
|---|---|
| Vorwort | Dr. Ehsan Adeli |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-80323-052-5 / 1803230525 |
| ISBN-13 | 978-1-80323-052-8 / 9781803230528 |
| Zustand | Neuware |
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