How the machine ‘thinks’: Understanding opacity in machine learning algorithms

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and
ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and
advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely
on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a
distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical
illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to
apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code
as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in
a given application is a key to determining which of a variety of technical and non-technical solutions could help to
prevent harm.