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AI Research
arXiv CS.AI·Jun 12·5 min read
Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
TL;DR
Content moderation systems often fail to recognize culturally insensitive speech that affects minority communities. Mod-Guide, a tool developed using retrieval augmented generation (RAG), incorporates minority perspectives to enhance sensitivity in moderation.
✦ Why It Matters
Engineers can enhance AI moderation systems by integrating diverse cultural perspectives to improve sensitivity and accuracy.
Key Takeaways
- Incorporate minority perspectives in AI systems.
- Utilize retrieval augmented generation for context sensitivity.
- Conduct mixed-method evaluations for diverse feedback.
- Focus on restorative justice in content moderation design.
Full Summary
Language can marginalize or empower communities, particularly for Indigenous and religious minorities facing insensitive online speech. Mod-Guide was created to address this issue by integrating a culturally grounded corpus of insensitive speech, co-developed with community members, into moderation systems using retrieval augmented generation (RAG).
This approach allows the system to leverage contextual cues from lived experiences of minority groups. Mixed-method evaluations involving both minority and majority participants demonstrated that RAG-enhanced moderation responses were more contextually accurate.
Participants reported differing perceptions of these responses across ethnic lines, indicating improved sensitivity to minority viewpoints. This research contributes to human-computer interaction, AI ethics, and social computing by emphasizing restorative justice and inclusive design in content moderation systems.