QCRI/FanarGuard

Cultural Safety Dataset

Dataset Description

The Cultural Safety Dataset is a benchmark for evaluating culturally sensitive and culturally misaligned model outputs in Arabic and Middle Eastern contexts. It focuses on cases where model responses conflict with culturally dependent societal norms and values.

The dataset was developed as part of FanarGuard: a culturally-aware moderation filter for Arabic language models.

Dataset Construction

The dataset combines:

• 822 prompts identified from production logs of an Arabic-language chat interface.
• 84 regionally sensitive questions from the Arabic Safety Benchmark.
• 198 manually generated prompts.

Three bilingual (English–Arabic) annotators classified the prompts for cultural relevance. The final set contains:

Category	Number
Culturally dependent	1,008
Partially cultural	36
General safety	60

The 1,008 culturally dependent prompts cover eight categories:

• Family & Social Norms
• Gender Roles & Equality
• Health & Bodily Autonomy
• Legal & Governance Norms
• Identity & Minority Representation
• Sexuality & Gender Identity
• Political & Geopolitical Sensitivity
• Religious Insult & Blasphemy

Model Responses

Responses were generated using five models:

• GPT-4o
• Qwen-3-32B
• Gemma-3-27B-It
• Fanar-1-9B-Instruct
• ALLaM-7B-Instruct-Preview

The benchmark contains 1,451 question–answer pairs, which were evaluated by three bilingual annotators. 363 responses received a score below 3, indicating cultural misalignment.

Intended Use

This dataset is intended for:

• Evaluating culturally aware moderation filters
• Benchmarking Arabic language models
• Studying cultural alignment and safety
• Developing culturally informed safety classifiers

Limitations

The dataset focuses on Arabic and Middle Eastern contexts and does not represent all Arabic-speaking communities or cultural perspectives. Cultural norms vary across countries, communities, and individuals, and human annotations may involve subjective judgments.

Citation

If you use this dataset, please cite:

@inproceedings{fatehkia2026fanarguard,
  title={FanarGuard: a culturally-aware moderation filter for Arabic language models},
  author={Fatehkia, Masoomali and Altinisik, Enes and Sencar, Husrev Taha},
  booktitle={Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={7848--7869},
  year={2026}
}