In this paper, we present new empirical evidence to demonstrate the near impossibility for existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. We diagnose the technical shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of the ‘human,’ and provide the details of the ambiguities and complexities of annotating text as derogatory or dangerous, in a way to demonstrate the need for persistently involving communities in the process. This decolonial perspective of content moderation and the empirical details of the technical difficulties of annotating online hateful content emphasize the need for what we describe as “ethical scaling”. We propose ethical scaling as a transparent, inclusive, reflexive and replicable process of iteration for content moderation that should evolve in conjunction with global parity in resource allocation for moderation and addressing structural issues of algorithmic amplification of divisive content. We highlight the gains and challenges of ethical scaling for AI-assisted content moderation by outlining distinct learnings from our ongoing collaborative project, AI4Dignity.