README.md

metadata

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

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:

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.

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