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

Auditing Yelp's Business Ranking and Review Recommendation Through the Lens of Fairness