Practicing Trustworthy Machine Learning

Synopsis

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 stakeholdersHow to recognize and fix fairness concerns and privacy leaks in an ML pipelineHow to develop ML systems that are robust and secure against malicious attacksImportant systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

Copyright:

2023

Book Details

Book Quality:

Publisher Quality

ISBN-13:

9781098120238

Related ISBNs:

9781098120245, 9781098120276

Publisher:

O'Reilly Media

Date of Addition:

01/05/23

Copyrighted By:

Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar.

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet, Sociology

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.