Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
arXiv:2606.13397v1 Announce Type: cross Abstract: Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online. As content moderation increasingly depends on large language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive speech-language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility.
As AI-powered content moderation scales, the inherent biases and limitations of LLMs in understanding nuanced cultural contexts are becoming increasingly apparent, creating an imperative for more sophisticated systems.
This development highlights the critical need for culturally sensitive AI in content moderation, influencing platform governance, user retention, and potentially shaping diverse communities' engagement with online spaces.
LLMs for content moderation will likely evolve to incorporate explicit cultural and social context awareness, moving beyond purely linguistic pattern recognition to address implicit insensitivity.
- · Indigenous & minority communities
- · Social media platforms prioritizing inclusivity
- · Ethical AI developers
- · Researchers in AI fairness
- · Platforms with unsophisticated moderation
- · Generative AI models with biased training data
- · Content moderation services lacking cultural expertise
More accurate and equitable content moderation for minority communities will emerge.
Public trust and engagement from marginalized groups on online platforms may increase.
This could lead to a broader demand for culturally-aware AI across other sectors, influencing AI development standards globally.
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Read at arXiv cs.AI