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JOURNAL ARTICLE Algorithmic Content Moderation and Transparency in Social Media: A Comparative Analysis of Systemic and Procedural Transparency by Tanishq Abdus Subhan
Recommendation and content-moderation algorithms now govern most of what people encounter on social media, replacing simple chronological feeds with systems that rank, demote, and remove content largely out of public view (Gorwa et al., 2020). This article examines why that opacity has become a policy flashpoint, distinguishing between the curatorial function of ranking and recommendation algorithms and the removal function of content moderation, while noting that both raise related concerns about bias, inconsistency, and the limited ability of affected users to contest decisions (Klonick, 2018). It surveys the leading regulatory responses to this problem, focusing on the European Union's Digital Services Act and its enforcement record, the more fragmented and constitutionally constrained approach taken in the United States, the UK's risk-based Online Safety Act, and a contrasting judicial route to platform accountability recently taken by Brazil's Supreme Court. It then turns to the central disagreement in the field: whether mandating disclosure of algorithmic design choices helps or harms the information environment, and whether structured, audited access for vetted researchers offers a workable middle ground between full disclosure and none (Suzor et al., 2019). Drawing on this literature, the article proposes a modest distinction between systemic transparency (disclosure about how a system works in general) and procedural transparency (disclosure to an individual about a specific decision), and argues that current regulation is comparatively well developed on the former and underdeveloped on the latter. The article concludes that transparency is not a single policy lever but a bundle of distinct obligations, that enforcement rather than the mere existence of legislation determines real-world impact, and that procedural transparency toward individual users may matter as much as systemic transparency about how algorithms work in general (Jhaver et al., 2019).
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