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Part IV. What to Do About Algorithmic Bias from a Data Scientist’s Perspective
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Part IV. What to Do About Algorithmic Bias from a Data Scientist’s Perspective
by Tobias Baer
Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists
Cover
Front Matter
Part I. An Introduction to Biases and Algorithms
1. Introduction
2. Bias in Human Decision-Making
3. How Algorithms Debias Decisions
4. The Model Development Process
5. Machine Learning in a Nutshell
Part II. Where Does Algorithmic Bias Come From?
6. How Real-World Biases Are Mirrored by Algorithms
7. Data Scientists’ Biases
8. How Data Can Introduce Biases
9. The Stability Bias of Algorithms
10. Biases Introduced by the Algorithm Itself
11. Algorithmic Biases and Social Media
Part III. What to Do About Algorithmic Bias from a User Perspective
12. Options for Decision-Making
13. Assessing the Risk of Algorithmic Bias
14. How to Use Algorithms Safely
15. How to Detect Algorithmic Biases
16. Managerial Strategies for Correcting Algorithmic Bias
17. How to Generate Unbiased Data
Part IV. What to Do About Algorithmic Bias from a Data Scientist’s Perspective
18. The Data Scientist’s Role in Overcoming Algorithmic Bias
19. An X-Ray Exam of Your Data
20. When to Use Machine Learning
21. How to Marry Machine Learning with Traditional Methods
22. How to Prevent Bias in Self-Improving Models
23. How to Institutionalize Debiasing
Back Matter
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17. How to Generate Unbiased Data
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18. The Data Scientist’s Role in Overcoming Algorithmic Bias
Part IV
What to Do About Algorithmic Bias from a Data Scientist’s Perspective
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