Buying Options

Kindle Price: $44.80
includes tax, if applicable

These promotions will be applied to this item:

Some promotions may be combined; others are not eligible to be combined with other offers. For details, please see the Terms & Conditions associated with these promotions.

Deliver to your Kindle or other device

Deliver to your Kindle or other device

<Embed>

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet or computer – no Kindle device required. Learn more

Read instantly on your browser with Kindle Cloud Reader.

Using your mobile phone camera, scan the code below and download the Kindle app.

QR code to download the Kindle App

Enter your mobile phone or email address

Processing your request...

By pressing ‘Send link’, you agree to Amazon's Conditions of Use.

You consent to receive an automated text message from or on behalf of Amazon about the Kindle App at your mobile number above. Consent is not a condition of any purchase. Message and data rates may apply.

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

Follow the Author

Something went wrong. Please try your request again later.


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

3.8 out of 5 stars 4 ratings

Amazon Price
New from Used from
Kindle
$44.80
Due to its large file size, this book may take longer to download

Kindle Monthly Deals
Kindle Monthly Deals
New deals each month starting at $1.49. Learn more

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 Statistics.com, an Elder Research Company.

--This text refers to the paperback edition.

From the Inside Flap

A PRACTICAL GUIDE TO IDENTIFYING AND REDUCING BIAS AND UNFAIRNESS IN DATA SCIENCE

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

Follow authors to get new release updates, plus improved recommendations.
Brief content visible, double tap to read full content.
Full content visible, double tap to read brief content.

Peter Bruce is the President and Founder of the Institute for Statistics Education at Statistics.com, 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 Statistics.com, 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.

Customer reviews

3.8 out of 5 stars
3.8 out of 5
4 global ratings
5 star
70%
4 star 0% (0%) 0%
3 star 0% (0%) 0%
2 star 0% (0%) 0%
1 star
30%

Review this product

Share your thoughts with other customers

Top reviews from Australia

There are 0 reviews and 0 ratings from Australia

Top reviews from other countries

Keith J
1.0 out of 5 stars A few interesting points
Reviewed in the United States on 11 July 2021
Verified Purchase
One person found this helpful
Report abuse
Robert Jae Han
5.0 out of 5 stars a helpful guide for practitioners
Reviewed in the United States on 7 August 2021
Verified Purchase
One person found this helpful
Report abuse
Brandt Montour
5.0 out of 5 stars Useful guide for data scientists and managers in the financial services industry
Reviewed in the United States on 10 August 2021
Verified Purchase
Amazon Customer
5.0 out of 5 stars For Managers and Scientists Alike
Reviewed in the United States on 13 October 2021
Verified Purchase