Machine Learning for High-Risk Applications: Approaches to Responsible AI (Paperback)

The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.

This book describes approaches for responsible AI--a holistic approach for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

• Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
• Learn how to create a successful and impactful AI risk management practice
• Look at how existing roles at companies are evolving to incorporate responsible AI
• Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
• Engage with interactive resources on GitHub and Colab

過去十年間，人工智慧和機器學習（AI/ML）技術的廣泛應用已經成為事實。然而，在這些技術的廣泛實施中缺乏監管，導致了一些事故和有害結果，這些結果本應在適當的風險管理下可以避免。在我們能夠實現AI/ML的真正好處之前，從業人員必須了解如何減輕其風險。

本書描述了負責任的AI方法，這是一種綜合方法，旨在改善AI/ML技術、業務流程和文化能力，並建立在風險管理、網絡安全、數據隱私和應用社會科學的最佳實踐基礎上。作者Patrick Hall、James Curtis和Parul Pandey為那些希望改善組織、消費者和公眾的現實世界AI/ML系統結果的數據科學家創作了這本指南。

本書內容包括：
- 學習負責任AI的技術方法，包括可解釋性、模型驗證和調試、偏見管理、數據隱私和ML安全
- 學習如何建立一個成功且有影響力的AI風險管理實踐
- 瞭解公司內現有角色如何演變以納入負責任AI
- 獲得有關採用AI技術的現有標準、法律和評估的基本指南，包括新的NIST AI風險管理框架
- 通過GitHub和Colab參與互動資源

請注意，以上為書籍內容的翻譯，並非原文。