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

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...

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Detalles Bibliográficos
Otros Autores: Baer, Tobias, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: [New York] : Apress 2019.
Materias:
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.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630574706719
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
  • 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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