Empire Amplifier: Uncovering and Contesting the Prioritization of Colonial Content on Platforms Through Community-Informed Algorithmic Auditing
Nel Escher, Bakyt Yrysov, Ashley McDermott, Daniel Chechelnitsky, Hermela Berehan Benyam, Nikola Banovic
摘要
Though online platforms claim to amplify Indigenous voices, Indigenous communities are worried that these systems are instead eroding their language and culture. We conduct a community-informed algorithmic audit to explore whether online platforms sustain or endanger Indigenous cultural practice. First, we review our prior ethnographic research pertaining to the cultural anxieties of a specific Indigenous community, as Indigenous peoples are not a monolith. We consider concerns from adults in Kyrgyzstan who believe that Russia’s linguistic influence is expanding and threatening their language. Next, we construct and conduct an algorithmic audit in conversation with the community. Our audit investigates deep-seated fears among Kyrgyz caregivers that YouTube encourages their children to speak Russian instead of Kyrgyz, their heritage language. We measure how the YouTube recommendation algorithm prioritizes content across Indigenous and non-Indigenous languages for child users. Our results validate caregiver concerns, as we find that YouTube primarily recommends non-Kyrgyz content to Kyrgyz children, even when children signal clear preferences for Kyrgyz content. Reading our audit results alongside our ethnographic observations, the amplification of non-Kyrgyz material appears to encourage the offline uptake of colonial linguistic hierarchies—YouTube promotes Russian videos that Kyrgyz children quote and emulate, reinforcing their preference for Russian over Kyrgyz. Thus, recommendation systems can actively erode Indigenous cultural practice online and offline. Finally, we evaluate strategies to align platform behavior with Indigenous values. We identify effective end-user practices for reducing the proportion of Russian-language YouTube recommendations, like cross-generational device sharing. We encourage researchers to consider how algorithmic systems can reimpose oppressive power structures that decolonial efforts have sought to dismantle.
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