Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Learn risk assessment for machine learning frameworks in a global landscape
• Discover patterns for next-generation AI ecosystems for successful product design
• Make explainable predictions for privacy and fairness-enabled ML training

Book Description

AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent.

You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.

By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.

What you will learn

• Understand the threats and risks involved in ML models
• Discover varying levels of risk mitigation strategies and risk tiering tools
• Apply traditional and deep learning optimization techniques efficiently
• Build auditable and interpretable ML models and feature stores
• Understand the concept of uncertainty and explore model explainability tools
• Develop models for different clouds including AWS, Azure, and GCP
• Explore ML orchestration tools such as Kubeflow and Vertex AI
• Incorporate privacy and fairness in ML models from design to deployment

Who this book is for

This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.

以台灣讀者為目標，以下是翻譯結果:

「以隱私、公平性和風險評估功能為基礎，為可擴展和分散式系統打造道德的人工智慧專案，同時保持可解釋性和可持續性」

「購買印刷版或Kindle電子書，即可免費獲得PDF電子書」

「主要特點」

- 在全球範圍內學習機器學習框架的風險評估
- 探索下一代人工智慧生態系統的成功產品設計模式
- 進行可解釋的預測，以實現隱私和公平性的機器學習訓練

「書籍描述」

「人工智慧算法無所不在，用於從招聘到貸款決策的各種任務。由於人工智慧在決策過程中的廣泛應用，建立一個可解釋、負責任、透明和可信賴的人工智慧系統變得必要。通過《負責任人工智慧的平台和模型設計》，您將能夠使現有的黑盒模型變得透明。」

「您將能夠識別並消除模型中的偏見，處理由數據和模型限制引起的不確定性，並提供負責任的人工智慧解決方案。您將首先為傳統和深度學習機器學習模型設計道德模型，並在可持續的生產環境中部署它們。之後，您將學習如何建立數據管道，驗證數據集，以及在任何雲不可知框架中以安全和私密的方式設置組件微服務。然後，您將建立一個具有適當限制的公平和私密的機器學習模型，調整超參數並評估模型指標。」

「通過閱讀本書，您將了解遵守數據隱私和倫理法律的最佳實踐，以及數據匿名化所需的技術。您將能夠開發具有可解釋性的模型，將其存儲在特徵存儲中，並處理模型預測中的不確定性。」

「您將學到的內容」

- 了解機器學習模型所涉及的威脅和風險
- 探索不同級別的風險緩解策略和風險分級工具
- 高效應用傳統和深度學習優化技術
- 構建可審計和可解釋的機器學習模型和特徵存儲
- 了解不確定性的概念，並探索模型解釋工具
- 開發適用於AWS、Azure和GCP等不同雲端的模型
- 探索Kubeflow和Vertex AI等機器學習管控工具
- 從設計到部署中將隱私和公平性納入機器學習模型

「本書適合對機器學習有經驗的專業人士，他們希望了解機器學習模型和框架的風險和洩漏，並學習開發和使用可重複使用的組件，以減少在建立和維護人工智慧生態系統方面的工作量和成本。」

1. Risks and Attacks on ML Models
2. The Emergence of Risk-Averse Methodologies and Frameworks
3. Regulations and Policies Surrounding Trustworthy AI
4. Privacy Management in Big Data and Model Design Pipelines
5. ML Pipeline, Model Evaluation and Handling Uncertainty
6. Hyperparameter Tuning, MLOPS, and AutoML
7. Fairness Notions and Fain Data Generation
8. Fairness in Model Optimization
9. Model Explainability
10. Ethics and Model Governance
11. The Ethics of Model Adaptability
12. Building Sustainable, Enterprise-Grade AI Platforms
13. Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
14. Industry-Wide Use-cases

1. ML模型的風險和攻擊
2. 風險回避方法和框架的出現
3. 關於可信AI的法規和政策
4. 大數據和模型設計流程中的隱私管理
5. ML流程、模型評估和處理不確定性
6. 超參數調整、MLOPS和AutoML
7. 公平概念和公平數據生成
8. 模型優化中的公平性
9. 模型可解釋性
10. 倫理和模型治理
11. 模型適應性的倫理問題
12. 建立可持續的企業級AI平台
13. 可持續的模型生命周期管理、特徵存儲和模型校準
14. 行業廣泛應用案例