README.md

---
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - legal
  - bias-detection
  - robustness
size_categories:
  - 10K<n<100K
pretty_name: RobustBiasBench

dataset_preview: data/robustbiasbench_dataset.csv

dataset_info:
  features:
    - name: id
      dtype: int64
    - name: date
      dtype: string
    - name: bias_type
      dtype: string
    - name: normative_framing
      dtype: string
    - name: source
      dtype: string
    - name: policy
      dtype: string
    - name: bias_type_merged
      dtype: string
  download_size: 5300000
  dataset_size: 5300000
configs:
    - config_name: default
      data_files:
        - split: train
          path: data/robustbiasbench_dataset.csv
---
# RobustBiasBench Dataset Description

This document provides an overview of the features and labels included in the **RobustBiasBench** dataset, which consists of over 18,000 policy excerpts annotated for bias type and normative framing.

---

## 📄 Dataset Format

The dataset is stored in CSV format and contains the following columns:

| Column Name         | Description |
|---------------------|-------------|
| `id`                | Unique numeric ID for each policy excerpt |
| `date`              | Year of the policy (extracted from the source document) |
| `bias_type`         | Fine-grained original bias label assigned by annotators (e.g., `age`, `gender`, `citizenship`) |
| `normative_framing`| Whether the bias is presented implicitly or explicitly (`implicit`, `explicit`, or `no_bias`) |
| `source`            | URL or citation source for the original policy document |
| `policy`            | Text excerpt of the policy (typically 1–3 sentences) |
| `bias_type_merged`  | Mapped bias class used for evaluation: one of `group_1`, `group_2`, or `no_bias` |

---

## 🏷️ Label Description

### `bias_type`
Original annotation capturing specific bias domains:
- `age`, `gender`, `race/culture`, `religion`, `disability` → Group 2 (Demographic Bias)
- `economic`, `education`, `political`, `citizenship`, `criminal_justice` → Group 1 (Systemic Bias)
- `no_bias` → Procedural or neutral statements

### `bias_type_merged`
Mapped classes used for modeling:
- `group_1`: Systemic/institutional bias
- `group_2`: Demographic/identity-based bias
- `no_bias`: Factual or operational content

### `normative_framing`
Captures how bias is framed:
- `explicit`: Bias is directly stated (e.g., "women are not eligible")
- `implicit`: Bias is implied through structure or condition (e.g., "must be a citizen to apply")
- `no_bias`: Used only when `bias_type = no_bias`

---

## 📊 Class Distribution

The dataset includes the following number of annotated examples:

- **No Bias**: 6,017 examples  
- **Systemic Bias (group_1)**: 6,246 examples  
- **Demographic Bias (group_2)**: 6,141 examples  

This balance ensures that the model does not overfit to any single category and can learn to differentiate across nuanced cases of policy bias.

---

## 🔒 License

This dataset is released under the **CC BY-NC-SA 4.0 License** and is intended for academic use in fairness and robustness research.