Update README.md

README.md

@@ -1,60 +1,66 @@
-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
-description: >
-  RobustBiasBench consists of 18,404 U.S. policy text excerpts manually annotated
-  for bias type and normative framing. The dataset is designed to evaluate the
-  robustness of language models in fairness-critical tasks, with both clean and
-  perturbed versions of the data available.
+
+# 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:
-  file_type: csv
-  fields:
-    - id: "Unique numeric ID for each policy excerpt"
-    - date: "Year of the policy (extracted from the source document)"
-    - bias_type: "Original fine-grained label (e.g., age, gender, citizenship)"
-    - normative_framing: "Bias framing: implicit, explicit, or no_bias"
-    - source: "URL or citation of the original policy document"
-    - policy: "Text excerpt of the policy (typically 1–3 sentences)"
-    - bias_type_merged: "Mapped class: one of group_1, group_2, or no_bias"
+
+## 📄 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_schema:
-  bias_type:
-    group_1: ["economic", "education", "political", "citizenship", "criminal_justice"]
-    group_2: ["age", "gender", "race/culture", "religion", "disability"]
-    no_bias: ["procedural", "neutral"]
-  bias_type_merged:
-    values: ["group_1", "group_2", "no_bias"]
-  normative_framing:
-    values: ["explicit", "implicit", "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:
-  no_bias: 6017
-  group_1: 6246
-  group_2: 6141
+
+## 📊 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_details: >
-  The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+## 🔒 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.
+
 ---
 
-citation: >
-  @inproceedings{bonagiri2025robustbiasbench,
-    title={RobustBiasBench: Stress-Testing LLMs for Bias Detection with Textual Perturbations},
-    author={Bonagiri, Akash and [Co-authors]},
-    booktitle={NeurIPS Dataset Track},
-    year={2025}
-  }
+## 📬 Contact
+
+For questions or contributions, please contact [yourname@yourinstitution.edu].