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Machine Learning for Finance

You're reading from   Machine Learning for Finance Principles and practice for financial insiders

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789136364
Length 456 pages
Edition 1st Edition
Languages
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Authors (2):
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Jannes Klaas Jannes Klaas
Author Profile Icon Jannes Klaas
Jannes Klaas
James Le James Le
Author Profile Icon James Le
James Le
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Table of Contents (15) Chapters Close

Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
1. Neural Networks and Gradient-Based Optimization 2. Applying Machine Learning to Structured Data FREE CHAPTER 3. Utilizing Computer Vision 4. Understanding Time Series 5. Parsing Textual Data with Natural Language Processing 6. Using Generative Models 7. Reinforcement Learning for Financial Markets 8. Privacy, Debugging, and Launching Your Products 9. Fighting Bias 10. Bayesian Inference and Probabilistic Programming Index

Chapter 9. Fighting Bias

We like to think that machines are more rational than us: heartless silicon applying cold logic. Thus, when computer science introduced automated decision making into the economy, many hoped that computers would reduce prejudice and discrimination. Yet, as we mentioned earlier when looking at mortgage applications and ethnicity, computers are made and trained by humans, and the data that those machines use stems from an unjust world. Simply put, if we are not careful, our programs will amplify human biases.

In the financial industry, anti-discrimination is not only a matter of morality. Take, for instance, the Equal Credit Opportunity Act (ECOA), which came into force in 1974 in the United States. This law explicitly forbids creditors from discriminating applicants based on race, sex, marital status, and several other attributes. It also requires creditors to inform applicants about the reasons for denial.

The algorithms discussed in this book are discrimination machines...

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