You're reading from Machine Learning Security Principles Keep data, networks, users, and applications safe from prying eyes

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781804618851

Length 450 pages

Edition 1st Edition

Languages

Python

Tools

Google cloud buildpack

Concepts

Machine Learning

Author (1):

John Paul Mueller

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1 – Securing a Machine Learning System

2. Chapter 1: Defining Machine Learning Security FREE CHAPTER

3. Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets

4. Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks

5. Part 2 – Creating a Secure System Using ML

6. Chapter 4: Considering the Threat Environment

7. Chapter 5: Keeping Your Network Clean

8. Chapter 6: Detecting and Analyzing Anomalies

9. Chapter 7: Dealing with Malware

10. Chapter 8: Locating Potential Fraud

11. Chapter 9: Defending against Hackers

12. Part 3 – Protecting against ML-Driven Attacks

13. Chapter 10: Considering the Ramifications of Deepfakes

14. Chapter 11: Leveraging Machine Learning for Hacking

15. Part 4 – Performing ML Tasks in an Ethical Manner

16. Chapter 12: Embracing and Incorporating Ethical Behavior

17. Index

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Understanding ML fairness

Aside from ethical concerns and ensuring that your dataset lacks issues such as bias, it’s important that the dataset and its associated model deliver a fair result. It’s possible for a dataset to lack any sort of PII, features that could be linked to particular groups, and unnecessary features, and yet remain unfair. One of the most controversial and well-known examples of ML unfairness is the models used to assess the recidivism risk of individuals seeking release from prison. Fairness in Machine Learning – The Case of Juvenile Criminal Justice in Catalonia (https://blog.re-work.co/using-machine-learning-for-criminal-justice/) tells of only one incidence. The problem is extremely widespread, leading many to ask whether ML is capable of being fair in this scenario. The following sections explore ML fairness in more detail.