Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into recommendation and other information access scenarios is not a straightforward task. This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.
This document was originally published in RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems by Association for Computing Machinery. Copyright restrictions may apply. doi: 10.1145/3298689.3346964
Ekstrand, Michael D.; Burke, Robin; and Diaz, Fernando. (2019). "Fairness and Discrimination in Recommendation and Retrieval". RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems, 576-577. https://dx.doi.org/10.1145/3298689.3346964