Unifying Diversity and Fairness in Re-ranking via Economic Growth Theory

With the growing complexity of the available online information, search engines via rankings and recommender systems come to the rescue, providing suggestions to users about items of potential interest, from movies and products to news articles and even potential friends. Such results and suggestions aim at covering the user information needs and play an important role in guiding users' decisions and in forming their opinions. However, the same technology, if not used responsibly, may lead to discrimination, amplify potential biases in the original data, restrict transparency and strengthen unfairness. For example, consider scenarios in which models based on biased data produce results that abet violence, decrease diversity, or have an adverse impact on economic policies. While the potential benefits of rankings and recommenders are well-accepted and understood, the importance of using such systems in a fair manner has only recently attracted attention. In this tutorial, we cover recent advancements and highlight future research directions in this increasingly relevant research area.