README.md

metadata

title: Headlne
emoji: 🔥
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.23.1
app_file: app.py
pinned: false

Bias Bin: Bias Detection and Mitigation in Language Models

Bias Bin is an interactive Gradio-based web application for detecting and mitigating gender bias in narrative text. It uses a fine-tuned BERT model and counterfactual data augmentation techniques to highlight and analyze bias in NLP outputs.

🧠 Project Overview

This tool allows users to: • Detect gender bias in input text using a BERT-based classification model. • Explore counterfactual predictions by swapping gendered terms. • Visualize bias scores to understand model behavior. • Demonstrate bias mitigation through gender-swapped text examples.

This project was developed as part of a university coursework in Deep Learning & Generative AI.

📁 Repository Contents • app.py – Main Python file to launch the Gradio web app. • Evaluation&Results.ipynb – Notebook with experiments, model evaluations, and visualizations. • fine_tuned_model.zip – Zip file containing the fine-tuned BERT model (must be extracted). • requirements.txt – List of Python dependencies.

⚙️ Setup Instructions 1. Clone the Repository

git clone https://huggingface.co/spaces/aryn25/bias.bin cd bias.bin

2.	Install Dependencies

pip install -r requirements.txt

3.	Extract the Model

Unzip the fine_tuned_model.zip file and place the extracted folder in the project root. 4. Run the App

python app.py

5.	Open in Browser

Visit the Gradio URL printed in the terminal

📊 Methodology • Model: Fine-tuned BERT classifier trained on gender-labeled narrative datasets. • Bias Detection: Uses counterfactual data augmentation by swapping gendered words (e.g., “he” → “she”). • Metrics: Bias scores are computed based on prediction discrepancies between original and counterfactual samples.

📚 References

This project is built using foundational and peer-reviewed research on: • BERT and Transformer models • Gender bias in NLP • Counterfactual data augmentation • Bias mitigation techniques

Full citation list available in the project report.

📌 Authors

Created by Aryan N. Salge and team as part of the Deep Learning & Generative AI coursework at the National College of Ireland.

📄 License

This project is for educational and research purposes. Please cite appropriately if you use or adapt the work.