"Using Graph Neural Networks to Model Biased Crowdsourced Data for Urban Applications": Graph neural networks (GNNs) are widely used on graph-structured data in urban spatiotemporal forecasting applications, such as predicting infrastructure problems and weather events. In such settings, nodes have a true latent state (e.g., street condition) that is sparsely observed (e.g., via government inspection ratings). We more frequently observe biased proxies for the latent state (e.g., via crowdsourced reports) that correlate with resident demographics. We introduce a GNN-based model that uses both unbiased rating data and biased reporting data to predict the true latent state. We show that our approach can both recover the latent state at each node and quantify the reporting biases. We apply our model to a case study of urban incidents using reporting data from New York City 311 complaints across 141 complaint types and rating data from government inspections. We show (i) that our model's predictions are more correlated with ground truth latent states compared to prior work which trains models only on the biased reporting data, (ii) that our model's inferred reporting biases capture known demographic biases, and (iii) that our model's learned ratings capture correlations across locations and between complaint types. Especially in urban crowdsourcing applications, our analysis reveals a widely applicable approach for using GNNs and sparse ground truth data to estimate latent states. – Sidhika Balachandar


"Fostering Tech Entrepreneurialism & Entrepreneurial Infrastructure Among Marginalized Communities": Marginalized entrepreneurs and those at society’s fringes often lack access to entrepreneurial infrastructure such as business education, access to capital, and payment processing. Drawing from several multi-year interview case studies with digital sex workers, influencers, content creators, tech innovators, leaders, startup founders and impact investors, this presentation explores individual, institutional, and philanthropic approaches to addressing the lack of access marginalized populations have to tech entrepreneurship and representation in global startup culture. The first case study explores how marginalized populations exercise agency to grant themselves access to tech entrepreneurship and entrepreneurial infrastructure building despite facing structural discrimination, erasure, and exploitation. The second case study investigates strategies of a black woman founded and owned impact venture capital fund to strategize around the stigma associated with financing and developing infrastructure & ecosystems to support stigmatized startup founders. The third case study analyzes the effectiveness of a fellowship program backed by a philanthropic organization working to create social entrepreneurs who address the needs of NYC’s most vulnerable populations. I argue that mere inclusion into dominant, oppressive societal structures will not liberate structurally exploited, marginalized populations; thus the establishment of their own, more autonomous infrastructures serves as a more viable means to actualize agency and counteract the incessant harms inflicted by structural oppression. – Maya Mundell