  As recommender systems are being designed and deployed for an increasing
number of socially-consequential applications, it has become important to
consider what properties of fairness these systems exhibit. There has been
considerable research on recommendation fairness. However, we argue that the
previous literature has been based on simple, uniform and often uni-dimensional
notions of fairness assumptions that do not recognize the real-world
complexities of fairness-aware applications. In this paper, we explicitly
represent the design decisions that enter into the trade-off between accuracy
and fairness across multiply-defined and intersecting protected groups,
supporting multiple fairness metrics. The framework also allows the recommender
to adjust its performance based on the historical view of recommendations that
have been delivered over a time horizon, dynamically rebalancing between
fairness concerns. Within this framework, we formulate lottery-based mechanisms
for choosing between fairness concerns, and demonstrate their performance in
two recommendation domains.
