| --- |
| license: cc-by-4.0 |
| language: |
| - am |
| - ha |
| - ig |
| - om |
| - sn |
| - sw |
| - tw |
| - wo |
| - yo |
| - zu |
| task_categories: |
| - text-classification |
| tags: |
| - safety |
| - llm-safety |
| - african-languages |
| - red-teaming |
| pretty_name: AfriGuard |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: amharic |
| data_files: |
| - split: train |
| path: amharic/train.csv |
| - split: validation |
| path: amharic/val.csv |
| - split: test |
| path: amharic/test.csv |
| - config_name: hausa |
| data_files: |
| - split: train |
| path: hausa/train.csv |
| - split: validation |
| path: hausa/val.csv |
| - split: test |
| path: hausa/test.csv |
| - config_name: igbo |
| data_files: |
| - split: train |
| path: igbo/train.csv |
| - split: validation |
| path: igbo/val.csv |
| - split: test |
| path: igbo/test.csv |
| - config_name: oromo |
| data_files: |
| - split: train |
| path: oromo/train.csv |
| - split: validation |
| path: oromo/val.csv |
| - split: test |
| path: oromo/test.csv |
| - config_name: shona |
| data_files: |
| - split: train |
| path: shona/train.csv |
| - split: validation |
| path: shona/val.csv |
| - split: test |
| path: shona/test.csv |
| - config_name: swahili |
| data_files: |
| - split: train |
| path: swahili/train.csv |
| - split: validation |
| path: swahili/val.csv |
| - split: test |
| path: swahili/test.csv |
| - config_name: twi |
| data_files: |
| - split: train |
| path: twi/train.csv |
| - split: validation |
| path: twi/val.csv |
| - split: test |
| path: twi/test.csv |
| - config_name: wolof |
| data_files: |
| - split: train |
| path: wolof/train.csv |
| - split: validation |
| path: wolof/val.csv |
| - split: test |
| path: wolof/test.csv |
| - config_name: yoruba |
| data_files: |
| - split: train |
| path: yoruba/train.csv |
| - split: validation |
| path: yoruba/val.csv |
| - split: test |
| path: yoruba/test.csv |
| - config_name: zulu |
| data_files: |
| - split: train |
| path: zulu/train.csv |
| - split: validation |
| path: zulu/val.csv |
| - split: test |
| path: zulu/test.csv |
| --- |
| |
| # AfriGuard: Safety Evaluation Data for African Languages |
|
|
| AfriGuard is a human-annotated safety dataset covering **10 African languages**: Amharic, Hausa, Igbo, Oromo, Shona, Swahili, Twi, Wolof, Yoruba, and Zulu. Each example contains a culturally grounded scenario and prompt/response pair in English and the target language, labeled with majority-vote annotations (safety category and safe/unsafe) from three native-speaker annotators. |
|
|
|  |
|
|
| Paper: [AfriGuard: Safety and Robust Refusal For African Languages](https://arxiv.org/abs/2610.26474) |
|
|
| ## Pipeline |
|
|
|  |
|
|
| ## Splits |
|
|
| Each language config provides three splits: |
|
|
| | Split | Size (per language) | Description | |
| |---|---|---| |
| | `train` | ~950–980 rows | 50% of scenario-level data | |
| | `validation` | 20 rows | Few-shot pool: 10 categories × {safe, unsafe}. The **first 10 rows cover all 10 classes** (5 safe / 5 unsafe); rows 11–20 cover all 10 classes with the opposite label. | |
| | `test` | ~980–1000 rows | 50% of scenario-level data. **100% of test `scenario_id`s are unseen in train.** | |
| |
| Split properties: |
| - Train/test split at the **scenario level**: every test scenario is fully unseen in train. |
| - Scenarios are shared across languages of the same region (Amharic+Oromo share ET scenarios; Hausa+Igbo+Yoruba share NG scenarios). Scenario assignment is made **globally per language group**, so there is **no cross-language train/test leakage**. |
| |
| ## Categories |
| |
| 1. Criminal Activity & Exploitation |
| 2. Political & Societal Manipulation |
| 3. Violence & Physical Harm |
| 4. Systemic Risks |
| 5. Sexual & Exploitative Content |
| 6. Unsafe or High-Risk Advice |
| 7. Privacy & Data Misuse |
| 8. Hate, Harassment & Discrimination |
| 9. Ethical & Moral Risk |
| 10. Intellectual Property & Content Integrity |
| |
| ## Fields |
| |
| All language configs share the same schema: |
| |
| | Column | Description | |
| |---|---| |
| | `id` | Unique example id | |
| | `scenario_id` | Scenario grouping id | |
| | `scenario` | Full scenario description (context, severity levels, example prompts, AI safety application) | |
| | `country` | Country context of the scenario | |
| | `target_language` | Target language code | |
| | `prompt` / `response` | English prompt and model response | |
| | `prompt_translated` / `response_translated` | Final human-verified translations | |
| | `majority_vote_class` | Majority-vote safety category from 3 annotators | |
| | `majority_vote_safe_unsafe` | Majority-vote safe/unsafe label from 3 annotators | |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("israel/AfriGuard", "amharic") |
| train, val, test = ds["train"], ds["validation"], ds["test"] |
| |
| # Few-shot: the first 10 validation rows cover all 10 categories (5 safe / 5 unsafe) |
| fewshot_10 = val.select(range(10)) |
| ``` |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{azime2026afriguardsafetyrobustrefusal, |
| title={AfriGuard: Safety and Robust Refusal For African Languages}, |
| author={Israel Abebe Azime and Jesujoba O. Alabi and Iffat Maab and Alexander D. Cai and Tadesse Destaw Belay and Atnafu Lambebo Tonja and Jonas Kgomo and Folasade Alabi and Salomey Osei and Saminu Muhammad Aliyu and Nkechinyere Faith Aguobi and Bontu Fufa Balcha and Blessing Sibanda and Davis David and Mouhamadane Mboup and Daud Abolade and Neo Putini and Louis Powell and Kiant'e Brantley and Philipp Slusallek and Dietrich Klakow and Julia Kreutzer}, |
| year={2026}, |
| eprint={2610.26474}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2610.26474}, |
| } |
| ``` |
| |
