News  |  ,   |  July 1, 2020

Programming Fairness in Algorithms

Blog post by Matthew Stewart.
Published on Towards Data Science.

Excerpt:

Understanding and combating issues of fairness in supervised learning.

Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this.

Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories — indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this “discrimination” is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. [ . . . ]