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

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Baer, Tobias (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [New York, NY] : Apress, [2019]
Temas:
Research > Statistical methods.
Machine learning > Social aspects.
Recherche > Méthodes statistiques.
Apprentissage automatique > Aspect social.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.

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