---
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}
}
```