In the previous chapter, you saw how an algorithm works. In this chapter, I will review how an algorithm is developed; this obviously is hugely helpful in understanding the many ways biases can creep into algorithms. Also, seasoned data scientists may want to briefly glance at this chapter so that they are aware of my mental frame and terminology since I will be referencing both frequently going forward. One note on terminology: with the advent of machine learning, a whole new vocabulary has been introduced (e.g., observations have become instances, dependent ...
© Tobias Baer 2019
Tobias BaerUnderstand, Manage, and Prevent Algorithmic Biashttps://doi.org/10.1007/978-1-4842-4885-0_44. The Model Development Process
Tobias Baer1
(1)
Kaufbeuren, Germany
Get Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
