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Practical Fairness : Achieving Fair and Secure Data Models

MRP: Rs.1,225.00
Price in points: 1225 points
9789385889783
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All Indian Reprints of O'Reilly are printed in Grayscale

Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias. 

Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms. 

  • Identify potential bias and discrimination in data science models 
  • Use preventive measures to minimize bias when developing data modeling pipelines 
  • Understand what data pipeline components implicate security and privacy concerns 
  • Write data processing and modeling code that implements best practices for fairness 
  • Recognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning models 
  • Apply normative and legal concepts relevant to evaluating the fairness of machine learning models

Books

Author:
Aileen Nielsen
Binding:
Paperback
Condition Type:
New
Country Origin:
India
Edition:
First
Gift Wrap:
N
Leadtime to ship in days (default):
ships in 1-2 days
Leadtime to ship in days(if not in stock):
ships in 12-15 days
Pages:
384
Publication Date:
23/12/2020
Publisher:
Shroff/O'Reilly
Year:
2020

Dimensions

Dimensions (W x H x D):
7 x 9 x .7 inch

Table of Contents (9789385889783_toc.pdf, 170 Kb) [Download]