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The Resource Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists, Tobias Baer

Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists, Tobias Baer

Label
Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists
Title
Understand, manage, and prevent algorithmic bias
Title remainder
a guide for business users and data scientists
Statement of responsibility
Tobias Baer
Creator
Author
Subject
Language
eng
Summary
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 take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors--and originates in--these human tendencies. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. Youll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the larger sociological impact of bias in the digital era
Cataloging source
YDX
http://library.link/vocab/creatorName
Baer, Tobias
Dewey number
005.1
Illustrations
illustrations
Index
index present
LC call number
Q180.55.S7
LC item number
B34 2019
Literary form
non fiction
http://library.link/vocab/subjectName
  • Research
  • Computer algorithms
  • Machine learning
  • Objectivity
  • Discrimination
  • Discrimination
  • Objectivity
Label
Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists, Tobias Baer
Instantiates
Publication
Copyright
Note
Includes index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
24 cm
Extent
xiii, 245 pages
Isbn
9781484248843
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations (black and white)
System control number
  • on1091846682
  • (OCoLC)1091846682
Label
Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists, Tobias Baer
Publication
Copyright
Note
Includes index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
24 cm
Extent
xiii, 245 pages
Isbn
9781484248843
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations (black and white)
System control number
  • on1091846682
  • (OCoLC)1091846682

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