| --- |
| language: |
| - ar |
| - en |
| license: other |
| task_categories: |
| - text-classification |
| - text-generation |
| tags: |
| - cultural-safety |
| - arabic |
| - moderation |
| - safety |
| - middle-east |
| - cultural-alignment |
| pretty_name: Cultural Safety Dataset |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| features: |
| - name: Response |
| dtype: string |
| - name: Prompt |
| dtype: string |
| - name: Model |
| dtype: string |
| - name: Judge_Qwen2.5-72B-Instruct |
| dtype: string |
| - name: Judge_Qwen3-32B |
| dtype: string |
| - name: Judge_gemma-2-27b-it |
| dtype: string |
| - name: Judge_c4ai-command-r-plus |
| dtype: string |
| - name: Qwen2.5-72B-Instruct_score |
| dtype: int64 |
| - name: Qwen3-32B_score |
| dtype: int64 |
| - name: gemma-2-27b-it_score |
| dtype: float64 |
| - name: c4ai-command-r-plus_score |
| dtype: float64 |
| - name: FanarGuard-R |
| list: float64 |
| - name: FanarGuard-G-2B |
| list: float64 |
| - name: FanarGuard-G-4B |
| list: float64 |
| - name: Ann_1_score |
| dtype: string |
| - name: Ann_2_score |
| dtype: string |
| - name: Ann_3_score |
| dtype: string |
| - name: Judge_Average |
| dtype: float64 |
| - name: Ann_Average |
| dtype: float64 |
| - name: Taxonomy |
| dtype: string |
| - name: Data Source |
| dtype: string |
| - name: PAM |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 11833983 |
| num_examples: 1451 |
| download_size: 5304127 |
| dataset_size: 11833983 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| extra_gated_fields: |
| Full name: text |
| Institutional affiliation: text |
| Country you are located in: country |
| Contact email: text |
| I want to use this dataset for: text |
| I agree to use this dataset exclusively for research purposes: checkbox |
| I agree that I will not use this dataset for malicious purposes, including training models to generate harmful content or automating policy evasion: checkbox |
| I agree that the dataset creators and their affiliated institutions are not liable for any claims, damages, algorithmic failures, or reputational harm resulting from my use or interpretation of this data: checkbox |
| I certify that the information I have provided is true and accurate: checkbox |
| --- |
| |
| # 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](https://aclanthology.org/2026.eacl-long.368/)**. |
|
|
| ### 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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
