Fairness in Information Access: Emphasizing the Network
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2023
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Haverford College. Department of Computer Science
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Thesis
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Award
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eng
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Dark Archive until 2024-01-01, afterwards Tri-College users only
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Abstract
Social networks - systems of interconnected people - are fundamental to our being in the world; and one’s belonging and positionality within networks greatly determines one’s exposure to resources and to harms. Information, understood in a broad sense, is vehiculated through(out) social networks; and machine learning algorithms are increasingly involved in determining access to information. The use of machine learning algorithm for information spread has been shown to reproduce, perpetuate, and exacerbate existing inequalities. Efforts have thus been made to create ‘fairnessaware’ machine learning algorithms. But these algorithms have tended to focus on individuals and demographics, at the expense of looking at the network itself. I argue that because the very structure of a network encodes sensitive attributes such as demographics, and because network belonging and positionality can themselves be sources of harm as well as privilege, the network should be thought of as an agent. I focus on the need for ’fair’ machine learning algorithms to take into account the ways in which harm does not operate merely at the individual level, but always at the network level by developing an experimental framework around collateral consequences - second-order effects that I model by positing a wellbeing for each individual in a social network.