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.

《企業負責任的人工智慧》是一本全面指南，介紹了在企業中實施道德、透明和合規的人工智慧系統的關鍵概念、工具和技術，涵蓋了創建公平、強大和可追溯的機器學習模型的方法。

主要特點：

- 學習道德人工智慧原則、框架和治理
- 理解公平評估和偏見緩解的概念
- 掌握可解釋人工智慧和透明度

書籍描述：

《企業負責任的人工智慧》提供了一個全面的指南，教你如何在組織中實施道德、透明和合規的人工智慧系統。本書著重於理解可解釋、安全、道德、強大、透明、可審計和可解釋的機器學習模型等關鍵概念，並提供開發人員處理偏見、公平性和模型治理等複雜問題的技術和算法。讀者將深入了解FairLearn和InterpretML，以及Google的What-If工具、ML Fairness Gym、IBM的AI 360 Fairness工具、Aequitas和FairLearn等其他工具。本書涵蓋了負責任人工智慧的各個方面，包括模型可解釋性、模型漂移的監控和管理，以及合規標準建議。它提供了實用的見解，教你如何在企業環境中使用人工智慧治理工具，確保公平性、偏見緩解、可解釋性、隱私合規和隱私。讀者將探索由IBM、亞馬遜、谷歌和微軟等主要雲端人工智慧提供商提供的可解釋性工具包和公平性指標，並學習如何使用FairLearn進行公平性評估和偏見緩解。通過閱讀本書，你將掌握創建透明和可追溯的機器學習模型的工具和技術。

你將學到什麼：

- 理解在人工智慧中道德考慮的重要性，認識確保負責任人工智慧實踐的模型治理標準的重要性。
- 檢測和緩解數據和算法中的偏見，並認識在人工智慧決策中公平性的必要性。
- 認識促進道德人工智慧的責任規定的重要性，並了解人工智慧對社會的影響。
- 分析模型可解釋性的方法和工具，並應用它們來理解人工智慧模型的決策過程。
- 評估人工智慧合規標準，並確定它們在確保可信賴人工智慧中的角色。
- 利用人工智慧治理框架，制定全面的負責任人工智慧實踐方法。
- 利用雲端人工智慧可解釋性工具包，在人工智慧模型中建立透明度和負責任性。
- 理解AWS、GCP和Azure中負責任人工智慧的原則，並認識它們在促進道德人工智慧實踐中的作用。

本書適合數據科學家、機器學習工程師、人工智慧從業者、IT專業人員、業務利益相關者和人工智慧倫理學家，他們負責在組織中實施人工智慧模型。

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

以下是翻譯結果:

1. 可解釋和道德人工智慧入門


2. 算法失控 - 偏見的最佳節目


3. 打開算法黑盒


4. 模型監控的操作化


5. 模型治理 - 審計和合規標準與建議


6. 公平、負責和透明的企業入門套件


7. 解釋性工具包和公平度量 - AWS、GCP、Azure和AIF 360


8. 使用Microsoft FairLearn實現人工智慧系統的公平性


9. 公平評估和偏見緩解 - FairLearn和負責任的AI工具箱


10. 基礎模型、LLMs和Azure Open AI: 探索負責任人工智慧的領域