Abstract
Just how the invention of computers and the Internet has fundamentally changed our world, machine learning is suddenly enabling analytics to be almost everywhere. Where such rapid change occurs, we humans are of course also prone to exuberance, even hype, and we sometimes need to take a step back and take a deep breath in order to keep things in perspective.
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This is well illustrated by a hackathon I once staged where teams from all over the world built a credit score. In the development sample, the winning machine learning algorithm had a performance almost double of that of a logistic regression by a team that declared “robustness” their primary objective—but that advantage came crashing down to a paltry 2 Gini points for out-of-time validation, and upon closer inspection we realized that the machine learning model had engaged in “red lining,” a practice that is illegal in the US and heavily discriminates against various groups of people including many blacks.
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© 2019 Tobias Baer
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Baer, T. (2019). When to Use Machine Learning. In: Understand, Manage, and Prevent Algorithmic Bias. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4885-0_20
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DOI: https://doi.org/10.1007/978-1-4842-4885-0_20
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4884-3
Online ISBN: 978-1-4842-4885-0
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