  The trade-off between relevance and fairness in personalized recommendations
has been explored in recent works, with the goal of minimizing learned
discrimination towards certain demographics while still producing relevant
results.
  We present a fairness-aware variation of the Maximal Marginal Relevance (MMR)
re-ranking method which uses representations of demographic groups computed
using a labeled dataset. This method is intended to incorporate fairness with
respect to these demographic groups.
  We perform an experiment on a stock photo dataset and examine the trade-off
between relevance and fairness against a well known baseline, MMR, by using
human judgment to examine the results of the re-ranking when using different
fractions of a labeled dataset, and by performing a quantitative analysis on
the ranked results of a set of query images. We show that our proposed method
can incorporate fairness in the ranked results while obtaining higher precision
than the baseline, while our case study shows that even a limited amount of
labeled data can be used to compute the representations to obtain fairness.
This method can be used as a post-processing step for recommender systems and
search.
