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

TL;DR
This study audits YouTube's recommendation algorithm to assess its impact on Kyrgyz children's language use, revealing it favors Russian content and undermines Indigenous language preservation, with community-informed strategies to mitigate this.
Contribution
It introduces a community-informed algorithmic audit focusing on Kyrgyz language preservation, highlighting how platform recommendations reinforce colonial language dominance.
Findings
YouTube primarily recommends non-Kyrgyz content to Kyrgyz children.
Recommendations reinforce Kyrgyz children's uptake of colonial language ideologies.
Cross-generational device sharing can reduce Russian-language recommendations.
Abstract
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 ethnographic research pertaining to the cultural anxieties of a specific Indigenous community, as Indigenous peoples are not a monolith. We consider concerns from Kyrgyz communities who believe that platforms are expanding Russia's linguistic influence 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…
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