# Welcome to RumorMill

{% hint style="info" %}
RumorMill was founded and is maintained by ML researchers of [Ethical Spectacle Research](https://ethicalspectacle.org) and [The Vector Institute](https://vectorinstitute.ai/).
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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. 

***

### 🛠️ RumorMill Toolkit

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

{% tabs %}
{% tab title="Browser Extension" %}

### Fair-ly Extension (Pending Renaming)

Our Chrome extension, [Fair-ly](https://chromewebstore.google.com/detail/fair-ly/geoaacpcopfegimhbdemjkocekpncfcc), is a showcase of SOTA models. Anyone can run a bias analysis pipeline/dashboard on their webpage, no code required.

{% embed url="<https://chromewebstore.google.com/detail/fair-ly/geoaacpcopfegimhbdemjkocekpncfcc>" %}

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](https://ethical-spectacle-research.gitbook.io/rumormill/research/sequence-classification/binary) (sentence -> biased/fair).
* [Bias aspect classification](https://ethical-spectacle-research.gitbook.io/rumormill/research/sequence-classification/multi-class) (sentence -> gender bias, racial bias, ...).
* [Token classification](https://ethical-spectacle-research.gitbook.io/rumormill/research/ner/token-classification) of generalizations, unfairness, and stereotypes.

Try this interactive demo for a quick look:

{% @arcade/embed flowId="Du67JkGTIgON60NwyJfU" url="<https://app.arcade.software/share/Du67JkGTIgON60NwyJfU>" %}
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{% tab title="Python Package" %}
{% hint style="info" %}
Walk through and run all the pipelines in this [Google Colab Notebook 💻](https://colab.research.google.com/drive/1Vwd8GuMoJNOiKDwryVgf0QFS2zKVVNlf?usp=sharing)
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### Install Our Package:

```bash
pip install the-fairly-project
```

### How to Use The Pipeline:

```python
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:

```json5
{
'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']}
]
}
```

<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td></td><td><code>TextAnalyzer</code> Docs</td><td></td><td><a href="toolkit/python-package/textanalyzer-pipeline">textanalyzer-pipeline</a></td></tr><tr><td></td><td><code>MultimodalAnalyzer</code> Docs</td><td></td><td><a href="toolkit/python-package/multimodalanalyzer-pipeline">multimodalanalyzer-pipeline</a></td></tr><tr><td></td><td></td><td></td><td></td></tr></tbody></table>
{% endtab %}

{% tab title="Hosted APIs" %}

<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td></td><td></td><td>Binary Classification API</td><td><a href="../toolkit/hosted-apis#binary-bias-classification">#binary-bias-classification</a></td></tr><tr><td></td><td></td><td>Types-of-bias Classification API</td><td><a href="../toolkit/hosted-apis#fairlyaspects-types-of-bias">#fairlyaspects-types-of-bias</a></td></tr><tr><td></td><td></td><td>GUS-Net (Token Classification) API</td><td><a href="../toolkit/hosted-apis#token-classification-of-generalizations-unfairness-and-stereotypes">#token-classification-of-generalizations-unfairness-and-stereotypes</a></td></tr></tbody></table>
{% endtab %}
{% endtabs %}

{% hint style="info" %}
If you're interested in contributing to the open-source tool-kit, check out our [GitHub](https://github.com/Ethical-Spectacle/rumormill) and join our [Discord](https://discord.com/invite/Jn6TYxwRjy).
{% endhint %}

***

### 🧠 Learn

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Recent Papers</strong></td><td>Papers to cite ;)</td><td></td><td></td><td><a href="readme/recent-papers-timeline">recent-papers-timeline</a></td></tr><tr><td><strong>Binary Classification</strong></td><td>Classifying text sequences as "Biased" or "Fair."</td><td></td><td></td><td><a href="research/sequence-classification/binary">binary</a></td></tr><tr><td><strong>Multi-Class Classification</strong></td><td>Classifying text sequences into more specific classes.</td><td></td><td></td><td><a href="research/sequence-classification/multi-class">multi-class</a></td></tr><tr><td><strong>Named-Entity Recognitio</strong>n</td><td>Classifying tokens (words) that contain bias.</td><td></td><td></td><td><a href="research/ner/token-classification">token-classification</a></td></tr><tr><td><strong>Multimodal Classification</strong></td><td>Classifying image and text pairs for bias.</td><td></td><td></td><td><a href="research/multimodal">multimodal</a></td></tr><tr><td><strong>Discord</strong></td><td>Ask questions or share a project.</td><td></td><td></td><td><a href="https://discord.com/invite/Jn6TYxwRjy">https://discord.com/invite/Jn6TYxwRjy</a></td></tr></tbody></table>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ethical-spectacle-research.gitbook.io/rumormill/readme.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.