构建首个韩语多模态安全数据集，评估生成式AI在韩国社会文化中的风险。
AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI
- 基于专家共识建立35类风险分类体系，覆盖韩语社会文化特性。
- 发布1.1万条韩语多模态数据，含文本、图像、视频和音频。
- 采用三重标注+专家红队迭代，确保数据质量与真实性。
生成式AI的快速发展亟需可靠的安全部评估。然而，现有安全数据集主要以英语为主，难以捕捉非英语社会文化背景（如韩国）下的特定风险，且多局限于文本模态。为此，我们提出AssurAI，一个高质量可控的韩语多模态安全评估数据集。首先，由跨学科专家团队基于成熟框架构建了35种不同类型的AI风险分类体系，涵盖普遍性危害及韩国社会文化相关风险。其次，基于该分类体系，我们构建并发布了AssurAI，一个大规模韩语多模态数据集，包含11,480个实例，涵盖文本、图像、视频和音频。第三，采用严格的质控流程：两阶段构建（专家引导播种与众包扩展）、三重独立标注，以及迭代式专家红队测试。初步研究验证了AssurAI在评估近期大模型安全性方面的有效性。我们已公开发布AssurAI，以促进面向韩国社区的更安全、更可靠生成式AI系统发展。
原文摘要 · Abstract (English)
The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we introduce AssurAI, a new quality-controlled Korean multimodal dataset for evaluating the safety of generative AI. First, we define a taxonomy of 35 distinct AI risk factors, adapted from established frameworks by a multidisciplinary expert group to cover both universal harms and relevance to the Korean socio-cultural context. Second, leveraging this taxonomy, we construct and release AssurAI, a large-scale Korean multimodal dataset comprising 11,480 instances across text, image, video, and audio. Third, we apply the rigorous quality control process used to ensure data integrity, featuring a two-phase construction (i.e., expert-led seeding and crowdsourced scaling), triple independent annotation, and an iterative expert red-teaming loop. Our pilot study validates AssurAI's effectiveness in assessing the safety of recent LLMs. We release AssurAI to the public to facilitate the development of safer and more reliable generative AI systems for the Korean community.
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