Digital platforms offer a host of applications that facilitate connections in large-scale two-sided markets, both with and without money. People use Instagram to see interesting new content from creators, and creators use it to reach global audiences. Shops on Facebook and Instagram offer innovative new ways for businesses to reach an interested user base, and Facebook Marketplace can be used to directly connect local buyers and sellers. Finally, businesses use Meta’s advertising products to reach interested audiences for goods and services that the businesses offer.
An important concern when facilitating billions of matches between the two sides of a market, is whether in aggregate the outcome is desirable or fair. For example, content on social issues may predominantly see distribution amongst audiences with higher affinity to the position in the content. As a result, someone may not see diverse content represented in their social media feed. On the other side of the market, a popular creator on Instagram may see a lot of traffic because the content is known to be good, which may make it difficult for new creators to build and connect with an audience.
Defining appropriate notions of fairness in these contexts is an important but challenging problem. Beyond defining the right definition of fairness, algorithms that do matchings in these large-scale markets are by necessity distributed and can only make local decisions about matches, whereas the properties of fairness one often cares about are global in nature.
To foster further innovation in this area, and to deepen our collaboration with academia, Meta is pleased to invite faculty to respond to this call for research proposals pertaining to the aforementioned topics. We anticipate awarding a total of three awards, each in the $50,000 range. Payment will be made to the proposer's host university as an unrestricted gift.