task_categories:text-classification task_categories:token-classification license:creativeml-openrail-m doi:10.57967/hf/1050 region:us
---
license: creativeml-openrail-m
task_categories:
- text-classification
- token-classification
---

#### **Dataset Description**

**About the Dataset**:
This dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.

**Purpose**:
The dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.

**Origin**:
The dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.

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#### **Composition**

**Dataset Statistics**:
- Total entries: 7500


**Data Fields**:
- **biased_text (string)**: The original text containing biased content.
- **dimension (string)**: The broader category under which the bias can be classified, such as race, gender, religion, etc.
- **aspect (string)**: The specific perspective or angle of bias present in the text.
- **biased_profain_words (string)**: Words in the `biased_text` that are identified as profane or highly biased.
- **bias_label (string)**: The category or degree of bias ranging from mild, moderate, to severe.
- **debiased_text (string)**: The debiased version of the `biased_text` generated using GPT-4 and subsequently verified by 5 human annotators.



#### **Data Use and Limitations**

**Usage**:
This dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.

**Limitations**:
- The dataset, though comprehensive, may not encapsulate all types of biases.
- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.
- Labels might contain inherent subjectivities as they are determined based on annotator discretion.

