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Responsible Data Science: Transparency and Fairness in Algorithms by [Grant Fleming, Peter C. Bruce]

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Responsible Data Science: Transparency and Fairness in Algorithms 1st Edition, Kindle Edition

3.8 out of 5 stars 4 ratings

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Product description

From the Publisher

GRANT FLEMING is a Data Scientist at Elder Research Inc. His professional focus is on machine learning for social science applications, model interpretability, civic technology, and building software tools for reproducible data science.

PETER BRUCE is the Senior Learning Officer at Elder Research, Inc., author of several best-selling texts on data science, and Founder of the Institute for Statistics Education at, an Elder Research Company.

--This text refers to the paperback edition.

From the Inside Flap


Rapid advancements in data science are causing increasing alarm around the world as governments, companies, other organizations, and individuals put new technologies to uses that were unimaginable just a decade ago. Medicine, finance, criminal justice, law enforcement, communication, marketing and other functions are all being transformed by the implementation of techniques and methods made possible by progressively more obscure manipulations of larger and larger data sets. Almost every day, new stories of AI gone awry appear. What can be done to avoid these issues?

Responsible Data Science is an insightful and practical exploration of the ethical issues that arise when the newest AI technologies are applied to the largest and most sensitive data sets on the planet. The book walks you through how to implement and audit cutting-edge AI models in ways that minimize the risks of unanticipated harms. It combines detailed technical analysis with perceptive social observations to offer data scientists a real-world perspective on their field.

The inability to explain how an artificial intelligence model uses inputs can jeopardize the willingness of regulators to even consider whether these technologies comply with existing and future regulatory and legal requirements. In this book you’ll learn how to improve the interpretability of AI models, and audit them to reduce bias and unfairness, thereby inspiring greater confidence in the minds of customers, employees, regulators, legislators and other stakeholders.

Perfect for data science practitioners, statisticians, software engineers, and technically aware managers and solutions architects, Responsible Data Science will also earn a place in the libraries of regulators, lawyers, and policy makers whose decisions will determine how and when data solutions are implemented.

This groundbreaking book also covers:

  • The various types of ethical challenges confronting modern day data scientists
  • How the adoption of “black box” models can aggravate issues of model transparency, bias, and fairness
  • How moral concepts like fairness translate (or fail to translate) into a modeling context
  • How model-agnostic methods can be used to make models more interpretable, identify issues of bias, and mitigate the bias discovered
--This text refers to the paperback edition.

Product details

  • ASIN ‏ : ‎ B093B8Z4YK
  • Publisher ‏ : ‎ Wiley; 1st edition (21 April 2021)
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 31005 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Print length ‏ : ‎ 282 pages
  • Customer Reviews:
    3.8 out of 5 stars 4 ratings

About the author

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Peter Bruce is the President and Founder of the Institute for Statistics Education at, a privately-owned online educational institution in Arlington, VA. Founded in 2002, the Institute specializes in introductory and graduate level online education in statistics, optimization, risk modeling, predictive modeling, data mining, and other subjects in quantitative analytics.

Prior to founding, in partnership with the noted economist Julian Simon, Peter continued and commercialized the development of Simon's Resampling Stats, a tool for bootstrapping and resampling. In his work at Cytel Software Corp., he developed Box Sampler along similar lines, and helped bring XLMiner, a data mining add-in for Excel, to market. He has authored a number of journal articles in the area of resampling, and is a co-author (with Galit Shmueli and Nitin Patel) of "Data Mining for Business Intelligence"​ (Wiley, 2nd ed. 2010). He is also the author of "Introductory Statistics and Analytics"​ (Wiley, 2014). Early in his career, he co-authored (with D. Traynham) a noted review of airline deregulation in the National Review (May, 1980).

Peter's role at the Institute centers on course development and faculty recruitment - there are over 60 faculty members from around the world who are published experts in their fields; most teach from their own texts. He also teaches a course on resampling methods.

Peter has degrees in Russian from Princeton and Harvard, and an MBA from the University of Maryland; he is an autodidact in the area of statistics. Prior to his work in statistics, Peter worked in the US diplomatic corps as a Foreign Service Officer.

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1.0 out of 5 stars A few interesting points
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5.0 out of 5 stars a helpful guide for practitioners
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5.0 out of 5 stars Useful guide for data scientists and managers in the financial services industry
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