Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable.

Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.

You'll learn:

• Methods to explain ML models and their outputs to stakeholders
• How to recognize and fix fairness concerns and privacy leaks in an ML pipeline
• How to develop ML systems that are robust and secure against malicious attacks
• Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

隨著人工智慧在醫學、法律和國防等高風險領域的使用越來越廣泛，組織花費大量時間和金錢來確保機器學習模型的可信度。許多關於這個主題的書籍深入探討了理論和概念。本指南提供了一個實用的起點，幫助開發團隊製作出更安全、更強大、更公正且更易解釋的模型。

作者Yada Pruksachatkun、Matthew McAteer和Subhabrata Majumdar將學術文獻中的最佳實踐轉化為建立工業級可信機器學習系統的藍圖。通過閱讀本書，工程師和數據科學家將獲得在嘈雜、混亂且常常充滿敵意的世界中發布可信機器學習應用所需的基礎。

你將學到以下內容：

- 向利益相關者解釋機器學習模型及其輸出的方法
- 如何識別並修復機器學習流程中的公平性問題和隱私洩漏
- 如何開發能夠抵禦惡意攻擊的強大且安全的機器學習系統
- 重要的系統考慮因素，例如如何管理信任債務以及哪些機器學習障礙需要人為干預

Yada Pruksachatkun is a machine learning scientist at Infinitus, a conversational AI startup that automates calls in the healthcare system. She has worked on trustworthy natural language processing as an Applied Scientist at Amazon, and led the first healthcare NLP initiative within mid-sized startup ASAPP. She did research transfer learning in NLP in graduate school at NYU and was advised by Professor Sam Bowman.

Matthew McAteer is the creator of 5cube Labs, an ML consultancy that has worked with over 100 companies in industries ranging from architecture to medicine to agriculture. Matthew worked with the Tensorflow team at Google on probabilistic programming, and previously worked in biomedical research in labs at MIT and Harvard Medical School.

Subhabrata (Subho) Majumdar is a Senior Applied Scientist at Splunk. Previously, he spent 3 years in AT&T, where he led research and development on ethical AI. Subho deeply believes in the power of data to bring about positive changes in the world---he has cofounded the Trustworthy ML Initiative, and has been a part of multiple successful industry-academia collaborations in the data for good space. Subho holds a PhD in Statistics from the University of Minnesota.

Yada Pruksachatkun 是 Infinitus 的機器學習科學家，Infinitus 是一家專注於醫療保健系統中自動化通話的對話式人工智慧初創公司。她曾在亞馬遜擔任應用科學家，致力於可信賴的自然語言處理，並在中型初創公司 ASAPP 內領導了首個醫療保健自然語言處理計畫。她在紐約大學攻讀研究生時，研究過自然語言處理中的轉移學習，並由 Sam Bowman 教授指導。

Matthew McAteer 是 5cube Labs 的創始人，5cube Labs 是一家機器學習顧問公司，已與超過100家公司合作，涵蓋建築、醫學和農業等多個行業。Matthew 曾在 Google 的 Tensorflow 團隊上從事概率編程的工作，之前在麻省理工學院和哈佛醫學院的實驗室從事生物醫學研究。

Subhabrata (Subho) Majumdar 是 Splunk 的高級應用科學家。之前，他在 AT&T 工作了3年，負責道德人工智慧的研究和開發。Subho 深信數據的力量可以帶來世界上的積極變革，他共同創辦了可信賴的機器學習計畫，並參與了多個成功的產學合作項目，致力於數據為善的領域。Subho 擁有明尼蘇達大學的統計學博士學位。