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Practical Fairness
Book

Practical Fairness

Achieving Fair and Secure Data Models

O'Reilly, 2020 más...


Editorial Rating

9

Qualities

  • Scientific
  • Concrete Examples
  • For Experts

Recommendation

Machine learning is becoming ubiquitous. This branch of artificial intelligence works by teaching a computer program what correct output looks like. This powerful method raises questions regarding fair outcomes for the people machine learning (ML) affects. Software engineer and attorney Aileen Nielsen examines different kinds of fairness and how training data and algorithms can promote them. For those developing machine learning models, she provides useful examples in Python.

Take-Aways

  • Fairness is about who gets what and how that’s decided. 
  • To get fair results, start with fair data. 
  • Train your data model to increase fairness at different stages of the process.

About the Author

Software engineer and lawyer Aileen Nielsen combines work at a deep learning start-up with a fellowship in law and technology at ETH Zürich.


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