Designing Equitable Algorithms for the Web

Machine learning algorithms increasingly affect both our online and offline experiences. Researchers and policymakers, however, have rightfully raised concerns that these systems might inadvertently exacerbate societal biases. We provide an introduction to fair machine learning, beginning with a general overview of algorithmic fairness, and then discussing these issues specifically in the context of the Web. To measure and mitigate potential bias from machine learning systems, there has recently been an explosion of competing mathematical definitions of what it means for an algorithm to be fair. Unfortunately, as we show, many of the most prominent definitions of fairness suffer from subtle shortcomings that can lead to serious adverse consequences when used as an objective. To illustrate these complications, we draw on a variety of classical and modern ideas from statistics, economics, and legal theory. We further discuss the equity of machine learning algorithms in the specific context of the Web, focusing on search engines and e-commerce websites. We expose the different sources for bias on the Web and how they impact fairness. They include not only data bias, but also biases that are produced by data sampling, the algorithms per-se, user interaction and feedback loops that result from user personalization and content creation. All these lead to a vicious cycle that affects everybody. The content of this tutorial is mainly based in the work of the authors [1,2,3,4].