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 prompt/response pair in English and the target language, labeled with a safety top category, a safe/unsafe label, and majority-vote annotations from three native-speaker annotators.

Splits

Each language config provides three splits:

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 → train/test assignment is made globally per language group, so there is no cross-language train/test leakage: a scenario in any language's train (or val) never appears in any language's test.
• safe/unsafe is 50/50 in every split.
• top_category distribution is balanced between train and test (max per-category difference ≤ 0.4%).
• Annotator-agreement rates (match_category, match_safe_unsafe) are balanced between train and test.
• Per-split statistics are available in stat.csv.

Categories

10 safety top 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:

Usage

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))
fewshot_20 = val  # all 10 categories x safe/unsafe