  Web 2.0 recommendation systems, such as Yelp, connect users and businesses so
that users can identify new businesses and simultaneously express their
experiences in the form of reviews. Yelp recommendation software moderates
user-provided content by categorizing them into recommended and not-recommended
sections. Due to Yelp's substantial popularity and its high impact on local
businesses' success, understanding the fairness of its algorithms is crucial.
However, with no access to the training data and the algorithms used by such
black-box systems, studying their fairness is not trivial, requiring a
tremendous effort to minimize bias in data collection and consider the
confounding factors in the analysis.
  This large-scale data-driven study, for the first time, investigates Yelp's
business ranking and review recommendation system through the lens of fairness.
We define and examine 4 hypotheses to examine if Yelp's recommendation software
shows bias and if Yelp's business ranking algorithm shows bias against
restaurants located in specific neighborhoods. Our findings show that reviews
of female and less-established users are disproportionately categorized as
recommended. We also find a positive association between restaurants being
located in hotspot regions and their average exposure. Furthermore, we observed
some cases of severe disparity bias in cities where the hotspots are in
neighborhoods with less demographic diversity or areas with higher affluence
and education levels. Indeed, biases introduced by data-driven systems,
including our findings in this paper, are (almost) always implicit and through
proxy attributes. Still, the authors believe such implicit biases should be
detected and resolved as those can create cycles of discrimination that keep
increasing the social gaps between different groups even further.
