Binary

Classifying text sequences as biased/fair.

Overview of Task:

Binary classification is the foundation of many bias detection frameworks, and in this case refers to classifying an entire text sequence as "biased" or "unbiased."

This is typically implemented with an encoder-only model, such as BERT, to create encodings (i.e. contextual representations) that capture "the meaning" of a sentence, and can be passed to a classifier layer(s) with one output feature (for 0 to 1 probability of a single class: "Biased").

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🤖 Models:

UnBIAS Classifier

One of the UnBIAS findings is that ternary classification (see Multi-Class) is a stronger approach, but the binary classification model is just as good.

UnBIAS Classifier

UnBIAS is a framework started in 2023 by Raza. et al at the Vector Institute, and a refresh of the technology proposed in Dbias.

🤗 HF Space to Test UnBIAS Classifier

Base Model: bert-base-uncased Dataset: BEAD (3.67M rows)

🤗 Hugging Face Model

newsmediabias/UnBIAS-classifier · Hugging Facehuggingface

📄 Research Paper

Dbias: Detecting biases and ensuring Fairness in news articlesarXiv.org

Use UnBIAS Classifier:

# pip install transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("newsmediabias/UnBIAS-classification-bert")
model = AutoModelForSequenceClassification.from_pretrained("newsmediabias/UnBIAS-classification-bert")

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer , device=0 if device.type == "cuda" else -1)
classifier("Anyone can excel at coding.")

Dbias

Dbias

Dbias proposed an architecture in 2022, for addressing news media bias, with a framework that utilized binary classification, named-entity recognition, bias masking, and word recommendation (Raza, et al.).

While reimplementations have made changes in approach, Dbias was a trailblazer, especially for binary classification (the first phase in the image above).

Base Model: bert-base-uncased, Dataset: MBAD Dataset

🤗Hugging Face Model

d4data/bias-detection-model · Hugging Facehuggingface

📄 Research Paper

Dbias: Detecting biases and ensuring Fairness in news articlesarXiv.org

Use Dbias Bias Classification:

Dbias has a PyPI package.

# pip install Dbias
# pip install https://huggingface.co/d4data/en_pipeline/resolve/main/en_pipeline-any-py3-none-any.whl
from Dbias.bias_classification import *

# returns classification label for a given sentence fragment.
classifier("Tall people are so clumsy.")

👀 Cool Finds

This is running list of cool binary classification models we've seen and want to learn more about. If you find one that should be here, send it to us on discord.

POLLCHECK/BERT-classifier · Hugging Facehuggingface

Fake/biased news binary classifier.

valurank/distilroberta-bias · Hugging Facehuggingface

Passes the "looks good to me" test with flying colors. Also has a quantized version on hf.

💾 Datasets:

BEADs

Bias Evaluation Across Domains (BEADs) Dataset

3.67M rows | 2024

The BEADs corpus was gathered from the datasets: MBIC, Hyperpartisan news, Toxic comment classification, Jigsaw Unintended Bias, Age Bias, Multi-dimensional news (Ukraine), Social biases.

It was annotated by humans, then with semi-supervised learning, and finally human verified.

It's one of the largest and most up-to-date datasets for bias and toxicity classification, though it's currently private so you'll need to request access through HuggingFace.

🤗HuggingFace Dataset

newsmediabias/news-bias-full-data · Datasets at Hugging Facehuggingface

📑 Contents

Fields	Description
text	The sentence or sentence fragment.
dimension	Descriptive category of the text.
biased_words	A compilation of words regarded as biased.
aspect	Specific sub-topic within the main content.
label	Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased, and neutral.
toxicity	Indicates the presence (True) or absence (False) of toxicity.
identity_mention	Mention of any identity based on words match.

While BEADs doesn't have a binary label for bias, the ternary labels (e.g. neutral, slightly biased, and highly biased) of the label field can categorized into biased (1), or unbiased (0). Additionally, the toxicity field contains binary labels.

📄 Research Paper

Navigating News Narratives: A Media Bias Analysis DatasetarXiv.org

Intended tasks for the BEADs dataset

GUS (Corpus)

Generalizations, Unfairness, and Stereotypes Dataset (Synthetic Corpus)

37.5k rows | 2024

The GUS dataset (released in the GUS-Net paper), is an entirely synthetic dataset. It was generated by Mistral 7B, and later used for named-entity recognition. The results of GUS-Net showed that the synthetic corpus was effective across domains, and less noise than authentic datasets.

🤗HuggingFace Dataset

ethical-spectacle/biased-corpus · Datasets at Hugging Facehuggingface

📑 Contents

Field	Description
biased_text	The full text fragment where bias is detected.
racial	Binary label, presence (1) or absence (0) of racial bias.
religious	Binary label, presence (1) or absence (0) of religious bias.
gender	Binary label, presence (1) or absence (0) of gender bias.
age	Binary label, presence (1) or absence (0) of age bias.
nationality	Binary label, presence (1) or absence (0) of nationality bias.
sexuality	Binary label, presence (1) or absence (0) of sexuality bias.
socioeconomic	Binary label, presence (1) or absence (0) of socioeconomic bias.
educational	Binary label, presence (1) or absence (0) of educational bias.
disability	Binary label, presence (1) or absence (0) of disability bias.
political	Binary label, presence (1) or absence (0) of political bias.
sentiment	The sentiment given to Mistral 7B in the prompt.
target_group	The group Mistral7B was told to prompt.
statement_type	Type of bias prompted (e.g. "stereotypes," "discriminatory language," "false assumptions," "offensive language," "unfair generalizations").

Mistral 7B was prompted to generate biased sentences, using the arguments in the table above. This means the entire thing should be biased. You may want to supplement this with a dataset of fair statements, and label all the sentences from this dataset with 1 (biased).

📄 Research Paper

GUS-Net: Social Bias Classification in Text with Generalizations,...arXiv.org

BABE

Bias Annotations By Experts (BABE)

4.12k records | 2023

Human annotated, and all annotators must agree. In its paper, BABE showed great results with BERT for sequence classification of news articles. While smaller than some other datasets, the annotations are very reliable (highly recommended as an external dataset for model eval).

🤗HuggingFace Dataset

mediabiasgroup/BABE · Datasets at Hugging Facehuggingface

📑 Contents

Fields	Description
text	The text fragment (few sentences or less).
outlet	The source of the text fragments.
label	0 or 1 (biased or unbiased).
topic	The subject of the text fragment.
news_link	URL to the original source.
biased_words	Full words contributing to bias, in a list.
type	Political sentiment (if applicable).

📄 Research Paper

Neural Media Bias Detection Using Distant Supervision With BABE --...arXiv.org

Fake News

Not added yet

POLLCHECK/BERT-classifier · Hugging Facehuggingface

Toxicity

Not added yet

Political

Not added yet

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How it Works:

Train your own binary classification model: 📝 Blog post - 💻 Training .ipynb

1. BERT (and other encoder models) process an input sequence into a encoding sequence as shown in the figure below, where self-attention heads encode the contextual words' meaning into each token representation.

2. These encodings are the foundation of many NLP tasks, and it's common (in BERT sequence classification) to then classify the CLS encoding into the desired classes (e.g. Biased, Neutral).

   1. The CLS token (pooler_output) is a built in pooling mechanism, but you can also use your own pooling mechanism (e.g. averaging all the representations for a mean-pooled representation).

3. bert-base-uncased has 768 output features (for each token) and we can pass the CLS token into a (768 -> 1) dense layer.

   1. This output logit of that classification head is activated (typically with a sigmoid or softmax function), for a probability that falls between 0-1.

4. A threshold is sometimes applied to the output (e.g. probability > 0.5 is "Biased").

Metrics:

When evaluating models' performance at binary classification, you should try to understand the way positive (biased), negative (neutral) fall into the categories: correct (true) predictions, and incorrect (false) predictions.

Your individual requirements will guide your interpretation (e.g. maybe you REALLY want to avoid false positives).

• Confusion Matrix: Used to visualize the levels of correct and incorrect classifications made, the goal

  
• Precision: $\frac{TP}{TP + FP}$

• Recall: $\frac{TP}{TP + FN}$

• F1 Score: $2 \times \frac{precision \times recall}{precision + recall}$

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