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Published in:
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2019 | OriginalPaper | Chapter

12. Options for Decision-Making

Author : Tobias Baer

Published in: Understand, Manage, and Prevent Algorithmic Bias

Publisher: Apress

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Abstract

In the first two parts of this book, you learned that the mechanics of algorithms expose them to many potential sources of bias, that biases are real and at times exceedingly harmful, and that algorithmic biases very often originate in real-world biases.

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previous chapter Algorithmic Biases and Social Media
next chapter Assessing the Risk of Algorithmic Bias
Footnotes
1
There are some intricacies to this case which are beyond this chapter but are important: a customer who is rejected by all financial institutions and therefore unable to take on any debt obviously can never demonstrate that he repays loans. One could argue that for lack of contradictory evidence, the algorithm’s assessment that this person should not get a loan therefore was correct (in fact, the fact that all other banks seem to concur could be considered supporting evidence for this hypothesis). The result, however, could be a perpetuating bias against this type of customer, and we therefore will discuss in a later chapter how to generate data to bust such a bias.
 
2
Basel Committee on Banking Supervision, “Studies on the validation of internal rating systems. Working Paper No. 14,” 2005.
 
3
Technically, Gini can be less than zero (but never more than 100); this happens if an algorithm is worse than random and you systematically can improve outcomes simply by doing the opposite of what the algorithm suggests. In this case, you simply turn your score upside down, and the Gini metric is positive again…
 
Metadata
Title
Options for Decision-Making
Author
Tobias Baer
Copyright Year
2019
Publisher
Apress
Book
Understand, Manage, and Prevent Algorithmic Bias

Print ISBN: 978-1-4842-4884-3

Electronic ISBN: 978-1-4842-4885-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-1-4842-4885-0_12

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