Welcome to RumorMill

A directory and implementation of SOTA bias detection research papers, all in one place.

RumorMill was founded and is maintained by ML researchers of Ethical Spectacle Research and The Vector Institute.

RumorMill is an open-source collection of resources, such as:

• Research papers

• Blogs and videos

• Datasets and models

Ethos: Bias-detection research should be accessible to users and developers of all levels.

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🛠️ RumorMill Toolkit

Here are a few tools we built for using state-of-the-art models in practice, something for everyone :).

Browser Extension

Fair-ly Extension (Pending Renaming)

Our Chrome extension, Fair-ly, is a showcase of SOTA models. Anyone can run a bias analysis pipeline/dashboard on their webpage, no code required.

Fair-ly - Chrome Web Store

It was created to open-the-door for new people to bias detection technology, by demonstrating it's strengths and weaknesses. The tasks it's intended to preform are:

• Binary bias classification (sentence -> biased/fair).
• Bias aspect classification (sentence -> gender bias, racial bias, ...).
• Token classification of generalizations, unfairness, and stereotypes.

Try this interactive demo for a quick look:

Python Package

Walk through and run all the pipelines in this Google Colab Notebook 💻

Install Our Package:

pip install the-fairly-project

How to Use The Pipeline:

from fairly import TextAnalyzer

analyzer = TextAnalyzer(bias="ternary", classes=True, top_k_classes=3, ner="gus")
result = analyzer.analyze("Tall people are so clumsy.")

Example Response:

{
'text': {
    'text': 'Tall people are so clumsy.', 
    'label': 'Slightly Biased', 
    'score': 0.6829080581665039, 
    'aspects': {
      'physical': 0.9650779366493225, 
      'gender': 0.024978743866086006, 
      'socioeconomic': 0.023334791883826256
    }
}, 
'ner': [
    {'token': 'tall', 'labels': ['B-STEREO', 'B-GEN', 'B-UNFAIR']}, 
    {'token': 'people', 'labels': ['I-STEREO', 'I-GEN', 'I-UNFAIR']}, 
    {'token': 'are', 'labels': ['I-STEREO']}, 
    {'token': 'so', 'labels': ['I-STEREO']}, 
    {'token': 'clumsy', 'labels': ['I-STEREO', 'B-UNFAIR', 'I-UNFAIR']},
    {'token': '.', 'labels': ['I-STEREO', 'I-UNFAIR']}
]
}

TextAnalyzer Docs

MultimodalAnalyzer Docs

Hosted APIs

Binary Classification API

Types-of-bias Classification API

GUS-Net (Token Classification) API

If you're interested in contributing to the open-source tool-kit, check out our GitHub and join our Discord.

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🧠 Learn

Recent Papers

Papers to cite ;)

Binary Classification

Classifying text sequences as "Biased" or "Fair."

Multi-Class Classification

Classifying text sequences into more specific classes.

Named-Entity Recognition

Classifying tokens (words) that contain bias.

Multimodal Classification

Classifying image and text pairs for bias.

Discord

Ask questions or share a project.