Fairness-aware Methods in Rankings and Recommenders
Evaggelia Pitoura, Kostas Stefanidis, Georgia Koutrika · 2021
We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this tutorial, we aim at presenting a toolkit of methods used for ensuring fairness in rankings and recommendations. Our objectives are two-fold: (a) to present related methods of this novel, quickly evolving and impactful domain, and put them into perspective, and (b) to highlight open challenges and research paths for future work.