数字皮肤，数字偏见：揭示基于肤色的LLM和表情符号嵌入中的偏见
Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings
AI总结：
本文研究了LLM和表情符号嵌入模型中基于肤色的偏见，发现表情符号模型在肤色表示上存在严重缺陷，揭示了潜在的偏见问题，呼吁开发者和平台进行审计和缓解。
摘要：
肤色表情符号在在线交流中对个人身份和社会包容至关重要。随着AI模型，特别是大语言模型（LLM）在网页平台上的作用日益增加，这些系统通过其对这些符号的表示可能加剧社会偏见的风险。本文首次对两种不同模型类别中肤色表情符号表示的偏见进行了大规模比较研究。我们系统地评估了专门的表情符号嵌入模型（emoji2vec，emoji-sw2v）与四个现代LLM（Llama，Gemma，Qwen和Mistral）。我们的分析首先揭示了关键性能差距：虽然LLM在支持肤色修饰符方面表现出稳健支持，但广泛使用的专门化表情符号模型却存在严重缺陷。更重要的是，对语义一致性、表示相似性、情感极性和核心偏见的多方面调查揭示了系统性差异。我们发现不同肤色的表情符号在情感和意义上存在偏斜和不一致，突显了这些基础模型中的潜在偏见。我们的发现强调了开发者和平台必须审计和缓解这些表示危害的紧迫性，以确保AI在互联网上的作用促进真正的公平，而不是强化社会偏见。
英文摘要：
Skin-toned emojis are crucial for fostering personal identity and social inclusion in online communication. As AI models, particularly Large Language Models (LLMs), increasingly mediate interactions on web platforms, the risk that these systems perpetuate societal biases through their representation of such symbols is a significant concern. This paper presents the first large-scale comparative study of bias in skin-toned emoji representations across two distinct model classes. We systematically evaluate dedicated emoji embedding models (emoji2vec, emoji-sw2v) against four modern LLMs (Llama, Gemma, Qwen, and Mistral). Our analysis first reveals a critical performance gap: while LLMs demonstrate robust support for skin tone modifiers, widely-used specialized emoji models exhibit severe deficiencies. More importantly, a multi-faceted investigation into semantic consistency, representational similarity, sentiment polarity, and core biases uncovers systemic disparities. We find evidence of skewed sentiment and inconsistent meanings associated with emojis across different skin tones, highlighting latent biases within these foundational models. Our findings underscore the urgent need for developers and platforms to audit and mitigate these representational harms, ensuring that AI's role on the web promotes genuine equity rather than reinforcing societal biases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
