Responsible AI in the Enterprise: Practical AI Risk Management for Explainable, Auditable, and Safe Models with Hyperscalers and Azure OpenAI
by: Adnan Masood (Author), Heather Dawe (Author)
Publisher finelybook 出版社: Packt Publishing – ebooks Account (September 11, 2023)
Language 语言: English
Print Length 页数: 252 pages
ISBN-10: 1803230525
ISBN-13: 9781803230528
Book Description
By finelybook
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
By finelybook
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 Al
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 AlF 360
8.Fairness in Al System with Microsoft FairLearn
9.Fairness assessment and bias mitigati onFairLearn and Responsible Al Toolbox
10.Foundation Models,LLMs,and Azure Open Al:Navigating the Landscape of Responsible Al
