UbuntuGuard：一种基于文化的政策基准，用于非洲语言中的公平AI安全
UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages
AI总结：
本文提出UbuntuGuard，一种针对非洲语言的公平AI安全政策基准，通过来自155名领域专家的对抗性查询，构建了基于本地规范和文化期望的政策和参考响应，评估守护模型的性能。
摘要：
当前的守护模型主要偏向西方，优化于高资源语言，使低资源非洲语言面临不断演变的危害、跨语言失败和文化不匹配。此外，大多数守护模型依赖于刚性的、预定义的安全类别，无法在多样化的语言和社会文化背景下泛化。实现稳健的安全需要灵活的、在运行时可执行的政策和基准，这些基准能反映本地规范、危害场景和文化期望。我们引入UbuntuGuard，这是首个基于政策的安全基准，用于非洲语言，由来自敏感领域的155名领域专家撰写的对抗性查询构建。从这些专家编写的查询中，我们推导出上下文特定的安全政策和参考响应，捕捉基于文化的危险信号，使守护模型的评估与政策对齐。我们评估了15个模型，包括7个通用大型语言模型和8个守护模型，涵盖三种不同的变体：静态、动态和多语言。我们的发现表明，现有的以英语为中心的基准高估了现实世界多语言安全，跨语言迁移提供了部分但不充分的覆盖，而动态模型虽然在推理时更能利用政策，但仍难以完全本地化非洲语言的环境。这些发现突显了对多语言、基于文化的安全基准的紧迫需求，以促进低资源语言中可靠和公平的守护模型的开发。
英文摘要：
Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-lingual failures, and cultural misalignment. Moreover, most guardian models rely on rigid, predefined safety categories that fail to generalize across diverse linguistic and sociocultural contexts. Achieving robust safety requires flexible, runtime-enforceable policies and benchmarks that reflect local norms, harm scenarios, and cultural expectations. We introduce UbuntuGuard, the first policy-based safety benchmark for African languages built from adversarial queries authored by 155 domain experts across sensitive fields, including healthcare. From these expert-crafted queries, we derive context-specific safety policies and reference responses that capture culturally grounded risk signals, enabling policy-aligned evaluation of guardian models. We evaluate 15 models, comprising seven general-purpose LLMs and eight guardian models across three distinct variants: static, dynamic, and multilingual. Our findings reveal that existing English-centric benchmarks overestimate real-world multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models, while better equipped to leverage policies at inference time, still struggle to fully localize African-language contexts. These findings highlight the urgent need for multilingual, culturally grounded safety benchmarks to enable the development of reliable and equitable guardian models for low-resource languages.
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。
