2026-09-18T21:06:20Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25986 2026-09-05T19:31:59Z ddc:004 open_access

Inducing Efficient and Equitable Professional Networks Through Link Recommendations Dwork, Cynthia Hays, Chris Hu, Lunjia Immorlica, Nicole Perdomo, Juan Professional networks Inequality Link Recommendations Professional networks are a key determinant of individuals’ labor market outcomes. They may also play a role in either exacerbating or ameliorating inequality of opportunity across social groups. We initiate an investigation into the positive role that a professional networking platform can play when network members have different degrees of off-platform privilege. In a theoretical model, we show that the set of link recommendation policies that reduce costs between privileged and unprivileged individuals yield equilibria that are welfare-improving over all possible equilibria, compared to those obtained when not recommending links or recommending some smaller fraction of cross-group links. We next investigate the implications of platforms that do not intervene on the network formation process. We show that, absent intervention, inequality can increase relative to starting privilege levels even without exogenous in-group preferences, confirming and complementing existing theoretical literature. Increased inequality emerges from the differential leverage privileged and unprivileged individuals have in forming connections due to their asymmetric ex ante prospects. This is a formalization of a source of inequality in the labor market which has not been previously explored. These two findings reveal a stark reality: professional networking platforms that fail to foster integration in the link formation process risk reducing the platform’s utility to its users and exacerbating existing labor market inequality. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Cynthia Dwork and Chris Hays and Lunjia Hu and Nicole Immorlica and Juan Perdomo 2026 Is Part Of LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2026.13 urn:nbn:de:0030-drops-259863 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.13 eng https://creativecommons.org/licenses/by/4.0/legalcode