Update README.md

README.md

@@ -1,11 +1,56 @@
----
-license: cc-by-4.0
+license: cc-by-nc-sa-4.0
 task_categories:
-- text-classification
+  - text-classification
 language:
-- en
+  - en
 tags:
-- legal
+  - legal
+  - bias-detection
+  - robustness
 size_categories:
-- 10K<n<100K
----
+  - 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.
+
+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"
+
+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"]
+
+class_distribution:
+  no_bias: 6017
+  group_1: 6246
+  group_2: 6141
+
+license_details: >
+  The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+  It is intended strictly for academic research use in evaluating fairness and robustness in NLP.
+
+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}
+  }