  Recommender system fairness has been studied from the perspectives of a
variety of stakeholders including content producers, the content itself and
recipients of recommendations. Regardless of which type of stakeholders are
considered, most works in this area assess the efficacy of fairness
intervention by evaluating a single fixed fairness criterion through the lens
of a one-shot, static setting. Yet recommender systems constitute dynamical
systems with feedback loops from the recommendations to the underlying
population distributions which could lead to unforeseen and adverse
consequences if not taken into account. In this paper, we study a connection
recommender system patterned after the systems employed by web-scale social
networks and analyze the long-term effects of intervening on fairness in the
recommendations. We find that, although seemingly fair in aggregate, common
exposure and utility parity interventions fail to mitigate amplification of
biases in the long term. We theoretically characterize how certain fairness
interventions impact the bias amplification dynamics in a stylized P\'{o}lya
urn model.
