EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
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3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Players report toxic behavior for short-term revenge and long-term community benefit but expect low success rates mediated by developer reputation, transparency, and community alignment.
Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.
citing papers explorer
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AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act
EvalAI providing pro/con arguments improves provision-level accuracy and reduces misclassification distance in DSA illegal content reporting under AI error conditions versus conventional XAI.
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"I Don't Have Faith in the Developers to Use My Feedback": Understanding Player Values and Expectancy for Reporting Systems in Video Games
Players report toxic behavior for short-term revenge and long-term community benefit but expect low success rates mediated by developer reputation, transparency, and community alignment.
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Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
Mod-Guide uses RAG with a community co-created corpus to make LLM moderation responses more contextually accurate for insensitive speech toward Bangladesh's Hindu and Chakma minorities, with mixed-method evaluation showing differences by ethnic background.