Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
License:
File size: 3,163 Bytes
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---
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.
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