Virtual Knowers and Knowledge: Structural Epistemic Injustices in Technological Systems

I aim to develop a framework to understand the distinct epistemic injustices that arise from technology. Data ethics scholars have investigated the various harms against marginalised groups that arise from emerging technologies like machine learning, social media, and the gig economy. Some have focused on, for example, how racial hierarchies are encoded in algorithms (Benjamin,

2019; Noble, 2018; Buolamwini & Gebru, 2018), and how AI systems are antithetical to trans liberation (Hoffman, 2021; Beauchamp, 2019). Meanwhile, philosophers have built on Fricker's (2007) notion of epistemic injustice, broadening it into theories of structural injustices (Anderson, 2012), and have applied this analysis to institutions like healthcare (Kidd & Carel, 2014).

I will focus on the overlooked intersection of the two in order to understand how unique structural epistemic injustices arise from seemingly objective, distributed, and agentless technological systems. I will develop a theory of how structural epistemic injustices arise from seemingly fair institutions, due to interactions with existing axes of oppression. I will then apply this to examine how

seemingly objective technologies are sites of structural epistemic injustices. This research will contribute to a deeper understanding of the specific harms caused by emerging technologies.