Understand, Manage, and Prevent Algorithmic Bias A Guide for Business Users and Data Scientists /

• Κύριος συγγραφέας: Baer, Tobias (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Algorithms.; Data mining.; Computer security.; Data structures (Computer science).; Data encryption (Computer science).; Algorithm Analysis and Problem Complexity.; Data Mining and Knowledge Discovery.; Systems and Data Security.; Data Storage Representation.; Cryptology.

The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not...

Πίνακας περιεχομένων:

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