  Collaborative filtering algorithms find useful patterns in rating and
consumption data and exploit these patterns to guide users to good items. Many
of the patterns in rating datasets reflect important real-world differences
between the various users and items in the data; other patterns may be
irrelevant or possibly undesirable for social or ethical reasons, particularly
if they reflect undesired discrimination, such as discrimination in publishing
or purchasing against authors who are women or ethnic minorities. In this work,
we examine the response of collaborative filtering recommender algorithms to
the distribution of their input data with respect to a dimension of social
concern, namely content creator gender. Using publicly-available book ratings
data, we measure the distribution of the genders of the authors of books in
user rating profiles and recommendation lists produced from this data. We find
that common collaborative filtering algorithms differ in the gender
distribution of their recommendation lists, and in the relationship of that
output distribution to user profile distribution.
