AI RESEARCH
Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework
arXiv CS.CL
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ArXi:2605.01416v1 Announce Type: cross The increasing scale and complexity of online platforms raises critical policy questions around harmful content, digital well-being, and user autonomy. Traditional content moderation systems rely on centralised, top-down rules, often failing to accommodate the subjective nature of harm perception. This paper proposes an LLM-based multi-agent personalised inference framework that filters content based on unique sensitivity profiles of individual users.