Red teaming for culturally grounded LLM evaluation: lessons learned from a Japanese team’s engagement with stereotype risks

AI and Society 41 (7):6477-6487 (2026)
  Copy   BIBTEX

Abstract

This paper examines how culturally grounded red teaming operates in a non-English context through a qualitative case study of Japan’s participation in the 2024 IMDA Red Teaming Challenge. While the challenge provides the shared institutional framework, the analysis focuses on Japan’s four-phase implementation, including expert-driven stereotype list development, in-person and virtual prompt generation, independent annotation, and interdisciplinary reflection. The findings show that Japan’s monolingual environment shaped the emergence of region-, gender-, and appearance-related stereotypes, and that participants intentionally selected Japanese-language prompts to test culturally embedded harms. The stereotype list lowered cognitive burden but sometimes oversimplified context, while annotation revealed recurring gray-zone dilemmas, such as distinguishing descriptive inequalities from normative generalizations and separating stereotypes from inference-level hallucinations. Interdisciplinary exchanges further showed that stereotype evaluation is an interpretive rather than purely technical task. The study positions culturally grounded red teaming as a sociotechnical process shaped by local norms and human judgment, offering methodological implications for more context-sensitive evaluation practices in non-English and culturally specific settings.

Other Versions

No versions found

Links

PhilArchive

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Analytics

Added to PP
2026-04-04

Downloads
14 (#1,971,653)

6 months
14 (#829,537)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations

References found in this work

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?Emily M. Bender, Timnit Gebru, Angelina McMillan-Major & Shmargaret Shmitchell - 2021 - Proceedings of the 2021 Acm Conference on Fairness, Accountability, and Transparency:610–623.

Add more references