  Information retrieval (IR) systems often leverage query data to suggest
relevant items to users. This introduces the possibility of unfairness if the
query (i.e., input) and the resulting recommendations unintentionally correlate
with latent factors that are protected variables (e.g., race, gender, and age).
For instance, a visual search system for fashion recommendations may pick up on
features of the human models rather than fashion garments when generating
recommendations. In this work, we introduce a statistical test for
"distribution parity" in the top-K IR results, which assesses whether a given
set of recommendations is fair with respect to a specific protected variable.
We evaluate our test using both simulated and empirical results. First, using
artificially biased recommendations, we demonstrate the trade-off between
statistically detectable bias and the size of the search catalog. Second, we
apply our test to a visual search system for fashion garments, specifically
testing for recommendation bias based on the skin tone of fashion models. Our
distribution parity test can help ensure that IR systems' results are fair and
produce a good experience for all users.
