Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness

This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts - that in some cases have caused loss of life - and develop models that are fair, transparent, safe, secure, and robust.
The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.

What You Will Learn

• Build AI/ML models using Responsible AI frameworks and processes
• Document information on your datasets and improve data quality
• Measure fairness metrics in ML models
• Identify harms and risks per task and run safety evaluations on ML models
• Create transparent AI/ML models
• Develop Responsible AI principles and organizational guidelines

Who This Book Is For
AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms

本書介紹了一個負責任的人工智慧（AI）框架，並引導您在機器學習（ML）生命週期的每個階段應用相應的流程，以減少和緩解AI技術中存在的風險和危害。如果正確且負責任地實施，AI能夠解決當今許多問題。本書幫助您避免負面影響，有些情況下甚至可能導致生命損失，並開發公平、透明、安全、可靠的模型。

本書的方法提高了您對AI技術中可能導致負面結果的錯誤的警覺，並提供了一個負責任的AI框架，以在ML中提供負責任和道德的結果。它從對責任、原則和數據的基礎元素進行了探討。接下來，提供了實施指南，解決公平性、透明度、安全性、隱私和可靠性等問題。本書幫助您在構建AI和ML模型時思考負責任，並引導您通過實際步驟，為最終用戶和客戶提供負責任的ML模型、數據集和產品。

您將學到什麼
- 使用負責任的AI框架和流程構建AI/ML模型
- 記錄數據集信息並提高數據質量
- 測量ML模型的公平性指標
- 根據任務識別危害和風險，對ML模型進行安全評估
- 創建透明的AI/ML模型
- 制定負責任的AI原則和組織指南

本書適合對象
- AI和ML從業人員，尋求在構建公平、透明和道德模型方面的指導
- 那些希望意識到AI算法可能導致無意識偏見和危害的錯誤的人
- 計劃制定促進公平和平等的自動化算法的法律、政策和規定的政策制定者

​Toju Duke is a Responsible AI Program Manager at Google with over 17 years of experience spanning across advertising, retail, not-for-profits, and tech industries. She designs Responsible AI programs focused on the development and implementation of Responsible AI frameworks, processes, and tools across Google's product and research teams. Toju is also the Founder of Diverse in AI, a community interest organization with a mission to provide inclusive and diverse AI through humanity. She provides consultation and advice on Responsible AI practices to organizations worldwide.

Toju Duke 是 Google 的負責 AI 負責任計畫經理，擁有超過 17 年的經驗，涵蓋廣告、零售、非營利組織和科技行業。她設計了負責任 AI 計畫，專注於在 Google 的產品和研究團隊中開發和實施負責任 AI 框架、流程和工具。Toju 也是 Diverse in AI 的創辦人，這是一個以人性為本的社區利益組織，旨在通過多樣性和包容性提供 AI。她向全球組織提供有關負責任 AI 實踐的諮詢和建議。