{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T21:32:33Z","timestamp":1776115953415,"version":"3.50.1"},"reference-count":52,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["957442"],"award-info":[{"award-number":["957442"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p> In this article, we present new empirical evidence to demonstrate the severe limitations of existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. Building on the collaborative coding project \u201cAI4Dignity\u201d we outline the ambiguities and complexities of annotating problematic text in AI-assisted moderation systems. We diagnose the shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of \u201cthe human\u201d. Presenting a decolonial critique of the \u201chuman vs machine\u201d conundrum and drawing attention to the structuring effects of coloniality on extreme speech, we propose \u201cethical scaling\u201d to highlight moderation process as political praxis. As a normative framework for platform governance, ethical scaling calls for a transparent, reflexive, and replicable process of iteration for content moderation with community participation and global parity, which should evolve in conjunction with addressing algorithmic amplification of divisive content and resource allocation for content moderation. <\/jats:p>","DOI":"10.1177\/20539517231172424","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T05:11:57Z","timestamp":1683609117000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":40,"title":["Ethical scaling for content moderation: Extreme speech and the (in)significance  of artificial intelligence"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3647-9570","authenticated-orcid":false,"given":"Sahana","family":"Udupa","sequence":"first","affiliation":[{"name":"University of Munich, Munchen, Germany"},{"name":"Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Munchen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2463-2588","authenticated-orcid":false,"given":"Antonis","family":"Maronikolakis","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Munchen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6058-0460","authenticated-orcid":false,"given":"Axel","family":"Wisiorek","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universitaet Muenchen (LMU Munich), Munchen, Germany"}]}],"member":"179","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"bibr1-20539517231172424","doi-asserted-by":"publisher","DOI":"10.1080\/03080188.2020.1840225"},{"key":"bibr2-20539517231172424","unstructured":"Barrett PM (2020) Who Moderates the Social Media Giants? 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