Hands on Data and Algorithmic Bias in Recommender Systems

Abstract

This tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation. Then, we practically show two use cases, addressing biases that lead to disparate exposure of items based on their popularity and to systematically discriminate against a legally-protected class of users. Finally, we cover a range of techniques for evaluating and mitigating the impact of these biases on the recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world platforms.