“Fairness Labels” in Feeds: Process Disclosures, Provenance, and User Choice Among Short‑Video Creators in Mainland China
2026-08-25
Algorithmically ranked feeds allocate attention in ways that can reproduce inequalities in visibility. We test whether a compact, feed-level process disclosure —a banner describing exposure-safeguard governance, paired with provenance cues and in-situ user-choice affordances—improves perceived procedural justice, distributive fairness, and legitimacy among short-video creators in mainland China. In a mobile-first online experiment with 1,154 research-verified PRC creators in the People’s Republic of China (PRC), participants viewed a realistic feed randomized to No label , Personalization notice , or Fairness disclosure ; within the fairness condition, we factorially varied frame (Safeguards/Parity/Quotas/Exposure-fairness), issuer provenance (platform vs. independent audit), and disclosure depth (minimal badge vs. brief rationale). The compound fairness disclosure increased perceived procedural justice, distributive fairness, and legitimacy relative to both controls and was accompanied by lower self-censorship and higher voice intentions. Effects were directionally stronger in civic/news contexts and among creators who self-identified as marginalized or previously down-ranked. Within-arm exploratory diagnostics suggested that quota-like parity framing may yield directionally lower legitimacy than exposure-fairness framing, while audit cues produced directionally positive but imprecise trust gains. Backfire checks found no implied-unfairness spillover for unlabeled items and no interaction with per-item AI-content labels, although AI labels reduced perceived accuracy. Together, these findings suggest that governance-aligned micro-disclosures can function as procedural visibility signals for creators, while underscoring the importance of verifiable claims and careful disclosure design.