Long-term Dynamics of Fairness Intervention in Connection Recommender Systems – Machine Learning Blog

We demonstrate how enforcing group-fairness in every recommendation slate separately does not necessarily promote equity in second order variables of interest like network size. Connection recommendation is at the heart of user experience in many online social networks. Given a prompt such as'People you may know', connection recommender systems suggest a list of users, and the recipient of the recommendation decides which of the users to connect with. In some instances, connection recommendations can account for more than 50% of the social network graph [1]. Depending on the platform, being connected to the right people is tied to important advantages such as job opportunities or increased visibility. While this makes it imperative to treat users fairly, it is far from obvious how fairness can be enforced or what it even means to have a'fair' system in this scenario.