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Understand, Manage, and Prevent Algorithmic Bias

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Cover of 'Understand, Manage, and Prevent Algorithmic Bias'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Bias in Human Decision-Making
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    Chapter 3 How Algorithms Debias Decisions
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    Chapter 4 The Model Development Process
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    Chapter 5 Machine Learning in a Nutshell
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    Chapter 6 How Real-World Biases Are Mirrored by Algorithms
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    Chapter 7 Data Scientists’ Biases
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    Chapter 8 How Data Can Introduce Biases
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    Chapter 9 The Stability Bias of Algorithms
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    Chapter 10 Biases Introduced by the Algorithm Itself
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    Chapter 11 Algorithmic Biases and Social Media
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    Chapter 12 Options for Decision-Making
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    Chapter 13 Assessing the Risk of Algorithmic Bias
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    Chapter 14 How to Use Algorithms Safely
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    Chapter 15 How to Detect Algorithmic Biases
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    Chapter 16 Managerial Strategies for Correcting Algorithmic Bias
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    Chapter 17 How to Generate Unbiased Data
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    Chapter 18 The Data Scientist’s Role in Overcoming Algorithmic Bias
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    Chapter 19 An X-Ray Exam of Your Data
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    Chapter 20 When to Use Machine Learning
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    Chapter 21 How to Marry Machine Learning with Traditional Methods
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    Chapter 22 How to Prevent Bias in Self-Improving Models
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    Chapter 23 How to Institutionalize Debiasing
Attention for Chapter 16: Managerial Strategies for Correcting Algorithmic Bias
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