README.md

---
license: mit
tags:
- AI
- Explainable-AI
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: title
    dtype: string
  - name: body
    dtype: string
  - name: source
    dtype: string
  - name: timestamp
    dtype: string
  - name: Misinfo_flag
    dtype: float64
  - name: type_of_misinfo
    dtype: string
  - name: type_reddit
    dtype: string
  - name: topic
    dtype: string
  - name: subtopic
    dtype: string
  - name: entities
    dtype: string
  - name: Polarization_flag
    dtype: string
  - name: "\tMisinfo_flag"
    dtype: float64
  - name: type_of_content
    dtype: string
  - name: potential_prompt0
    dtype: string
  - name: hashtags
    dtype: string
  - name: gender
    dtype: string
  - name: sentiment_category
    dtype: string
  - name: Publisher
    dtype: string
  - name: subtitle
    dtype: string
  - name: prochoice_prolife
    dtype: string
  splits:
  - name: train
    num_bytes: 208694523
    num_examples: 240156
  download_size: 99346690
  dataset_size: 208694523
---

# 📊 Explainable AI Dataset: Bias, Misinformation, and Source Influence

[Dataset Development Github](https://github.com/Nastiiasaenko/Final-Project---Explainable-AI-)

This dataset provides a **comprehensive, metadata-enriched resource** for studying AI-generated content, tracing biases, and analyzing misinformation. It is designed to facilitate research in **Responsible AI, transparency, and content generation analysis**.

## 📌 Dataset Overview
- **Sources:** Verified news, social media (Reddit, Twitter), misinformation datasets
- **Key Attributes:**
  - `title`: Headlines from news, Reddit, and tweets
  - `body`: Full article or post content
  - `source`: Origin of content (e.g., news, Reddit)
  - `misinformation_flag`: Label for misinformation presence
  - `political_bias`: Classification of ideological leanings
  - `sentiment`: Sentiment label (positive, neutral, negative)
  - `named_entities`: People, organizations, and topics extracted
  - `demographics`: Indicators such as gender associations (where applicable)

## 🎯 Use Cases
This dataset enables:
- **Bias & Misinformation Analysis**: Investigate AI amplification of political bias and misinformation.
- **AI Content Tracing**: Examine how LLMs generate narratives based on real-world data.
- **Sentiment & Polarization Studies**: Compare AI-generated content with public discourse.
- **Prompt Engineering Research**: Develop structured prompts for bias evaluation.

## 🔗 Dataset Access
The dataset can be loaded directly using the `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("nastiiasaenko/Responsible-AI-Dataset")
```




## 📖 Citation

If you use this dataset, please cite:

Saenko, A. (2025). "Explainable AI Dataset: Bias, Misinformation, and Source Influence." Hugging Face Datasets.

---