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---
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.
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