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Understand, manage, and prevent algorithmic bias : a guide for business users and data scientists /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York, NY] :
Apress,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Baer, Tobias, |e author. | |
| 245 | 1 | 0 | |a Understand, manage, and prevent algorithmic bias : |b a guide for business users and data scientists / |c Tobias Baer. |
| 264 | 1 | |a [New York, NY] : |b Apress, |c [2019] | |
| 264 | 4 | |c ©2019 | |
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| 520 | |a 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. You'll 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. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on July 18, 2019). | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 with Traditional Methods -- 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Research |x Statistical methods. | |
| 650 | 0 | |a Machine learning |x Social aspects. | |
| 650 | 6 | |a Recherche |x Méthodes statistiques. | |
| 650 | 6 | |a Apprentissage automatique |x Aspect social. | |
| 650 | 7 | |a Research |x Statistical methods. |2 fast |0 (OCoLC)fst01095242 | |
| 776 | 0 | 8 | |i Print version: |a Baer, Tobias. |t Understand, Manage, and Prevent Algorithmic Bias : A Guide for Business Users and Data Scientists. |d Berkeley, CA : Apress L.P., ©2019 |z 9781484248843 |
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