New Book Explores Transparency and Fairness in Algorithms

A new book by Peter Bruce and Grant Fleming explores the most serious prevalent ethical issues in data science.  Responsible Data Science: Transparency and Fairness in Algorithms delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society.

Both data science practitioners and managers of analytics teams will learn how to:

  • Improve model transparency, even for black box models
  • Diagnose bias and unfairness within models using multiple metrics
  • Audit projects to ensure fairness and minimize the possibility of unintended harm

Responsible Data Science is the perfect resource for data science practitioners and technically inclined managers, software developers, and statisticians.

The authors will also be teaching a course, The Ethical Practice of Data Science, to provide guidance and tools to build better models that avoid bias and unfairness.

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About the Authors

Peter Bruce founded the Institute for Statistics Education at 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.


Grant Fleming is a Data Scientist at Elder Research Inc. During his time at Elder Research, he has worked with clients in both government and the private sector on statistical testing, data asset creation, predictive analytics, and latent variable modeling. He has given multiple talks on machine learning interpretability and fairness and is working on developing software packages for creating reproducible and interpretable black box models.