Diversity and Inequality in Social Networks
Remote Talk
Past Talk
Ana-Andreea Stoica
Ph.D. candidate, Columbia University
Friday
Nov 12, 2021
Watch video
2:00 pm
177 Huntington Ave
Online
11th floor
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Online social networks often mirror inequality in real-world networks, from historical prejudice, economic or social factors. Such disparities are often picked up and amplified by algorithms that leverage social data for the purpose of providing recommendations, diffusing information, or forming groups. In this talk, we'll discuss possible explanations for algorithmic bias in social networks, specifically in (i) recommendation algorithms and (ii) the influence maximization problem. Using the preferential attachment model with unequal communities, we'll characterize the relationship between homophily, network centrality, and bias through the power-law degree distributions of the nodes, and study the conditions in which diversity interventions can actually yield more efficient and equitable outcomes. In addition, we’ll see that recommendations which use the neighborhood of individuals may hinder the incoming connections of minority groups, while algorithms that use centrality-based measures in diffusing information may leave minorities out of the loop. In addition, we’ll discuss a set of heuristics that leverage the network structure to maximize the diffusion of a message while not creating disparate impact among participants based on sensitive demographics like gender or race. To wrap up, I’ll discuss research outputs from the Mechanism Design for Social Good initiative, in which I have been serving as a co-organizer for the last two years.

About the speaker
About the speaker
Ana-Andreea Stoica is a Ph.D. candidate at Columbia University. Her work focuses on mathematical models, data analysis, and inequality in social networks. From recommendation algorithms to the way information spreads in networks, Ana is particularly interested in studying the effect of algorithms on people's sense of privacy, community, and access to information and opportunities. She strives to integrate tools from mathematical models—from graph theory to opinion dynamics—with sociology to gain a deeper understanding of the ethics and implications of technology in our everyday lives. Ana grew up in Bucharest, Romania, and moved to the US for college, where she graduated from Princeton in 2016 with a bachelor's degree in Mathematics. Since 2019, she has been co-organizing the Mechanism Design for Social Good initiative. For details, visit http://www.columbia.edu/~as5001/.
Ana-Andreea Stoica is a Ph.D. candidate at Columbia University. Her work focuses on mathematical models, data analysis, and inequality in social networks. From recommendation algorithms to the way information spreads in networks, Ana is particularly interested in studying the effect of algorithms on people's sense of privacy, community, and access to information and opportunities. She strives to integrate tools from mathematical models—from graph theory to opinion dynamics—with sociology to gain a deeper understanding of the ethics and implications of technology in our everyday lives. Ana grew up in Bucharest, Romania, and moved to the US for college, where she graduated from Princeton in 2016 with a bachelor's degree in Mathematics. Since 2019, she has been co-organizing the Mechanism Design for Social Good initiative. For details, visit http://www.columbia.edu/~as5001/.