When Algorithms Decide: Filtered Voices from Nepal
"When Algorithms Decide: Filtered Voices from Nepal" is a diagnostic report examining how automated content moderation systems shape what Nepali creators, journalists, and activists can say online. Drawing on platform transparency data from TikTok, YouTube, and Meta, alongside an online survey, a focus group discussion, a key informant interview, and a national roundtable conducted by Open Knowledge Nepal in 2026, the report maps what is currently knowable about algorithmic moderation in Nepal, and identifies where the evidence runs out. TikTok removed approximately 9.12 million Nepali videos across 2025, with proactive automated detection accounting for 99.6 to 99.9 percent of all removals. YouTube ranked Nepal among its highest-volume removal markets globally. Meta publishes zero Nepal-specific enforcement data and maintains no local contact. The report identifies three structural gaps, transparency, language, and accountability, and sets out concrete recommendations for platforms, government, and civil society. It is a diagnostic, not a verdict: a baseline for further research, policy engagement, and advocacy.
Key Insights
Enforcement at Scale, Accountability in Absence
TikTok removed approximately 9.12 million Nepali videos across 2025, nearly 1.9 million in the final quarter alone, with 99.6 to 99.9 percent removed by proactive automated detection rather than human review. YouTube ranked Nepal among the top 25 countries globally for removals, and Meta publishes no Nepal-specific enforcement data at all. Enforcement happens at volume; meaningful accountability does not.
Three Structural Gaps
Every finding points to the same three gaps. The transparency gap: platforms remove content at scale without publishing the language-level data needed to assess whether decisions are accurate or fair. The language gap: automated systems were never trained on Nepali, Maithili, or Newari, yet the consequences fall entirely on Nepali creators. The accountability gap: there is no dedicated authority, no local platform contact, and no functional appeal pathway. When something goes wrong, there is a form, and no one knows if it works.
What the Numbers Cannot Show
Community research surfaces the human texture behind the data. Creators discover they have been shadow-banned only by watching their own analytics. A journalist learns which words are unsafe by trial and error. Self-censorship becomes automatic, built into the creative process before content is even made. Language operates as a structural disadvantage, and appeals remain unknown to most and inaccessible to many.
A Global Pattern, Not a Local Exception
Nepal sits inside a documented global dynamic. Research across Africa, Latin America, South Asia, and the Middle East records the same pattern: English-built moderation systems deployed worldwide that consistently fail communities across the Global South. The language differs; the outcome is the same.
Concrete Recommendations for Three Actors
Platforms should publish language-level enforcement data, establish a real Nepal contact point and creator-facing grievance mechanisms, and invest in local-language training data. Government should make platform registration a genuine accountability mechanism, pursue a binding AI Act, and clearly distinguish regulation from removal. Civil society should build a public log of moderation incidents and develop Nepali-language literacy materials.
