Prior research on exposure fairness in the context of recommender systems has
focused mostly on disparities in the exposure of individual or groups of items
to individual users of the system. The problem of how individual or groups of
items may be systemically under or over exposed to groups of users, or even all
users, has received relatively less attention. However, such systemic
disparities in information exposure can result in observable social harms, such
as withholding economic opportunities from historically marginalized groups
(allocative harm) or amplifying gendered and racialized stereotypes
(representational harm). Previously, Diaz et al. developed the expected
exposure metric -- that incorporates existing user browsing models that have
previously been developed for information retrieval -- to study fairness of
content exposure to individual users. We extend their proposed framework to
formalize a family of exposure fairness metrics that model the problem jointly
from the perspective of both the consumers and producers. Specifically, we
consider group attributes for both types of stakeholders to identify and
mitigate fairness concerns that go beyond individual users and items towards
more systemic biases in recommendation. Furthermore, we study and discuss the
relationships between the different exposure fairness dimensions proposed in
this paper, as well as demonstrate how stochastic ranking policies can be
optimized towards said fairness goals.

研究推荐系统中的曝光公平性，提出了一种从消费者和生产者双方角度建模的公平度量，以缓解除了个体用户和物品的不公平之外的更系统性的偏见，进一步研究了曝光公平性维度之间的关系并演示了如何将随机排序策略优化为这些公平目标。