Year: 2026
Type: conference-paper
Abstract: Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and regionspecific regulations.However, building largescale, culturally grounded datasets is challenging due to limited resources and a scarcity of native annotators.Consequently, many safeguard models rely on machine translation of English datasets, often missing regional and cultural nuances.We present a novel agentic data-generation framework to scalably create authentic, region-specific safety datasets for Southeast Asia (SEA).On this foundation, we introduce the SEA-Guard family, the first multilingual safeguard models grounded in SEA cultural contexts.Evaluated across multiple benchmarks and cultural variants, SEA-Guard consistently outperforms existing safeguards at detecting regionally sensitive or harmful content while maintaining strong general safety performance.
Institution: Google (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166830490