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https://github.com/jai0212/cash-app-bias-busters
A project for CashApp to help reduce bias in ML models in transactions, credit card approvals and loans
https://github.com/jai0212/cash-app-bias-busters
Last synced: about 4 hours ago
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A project for CashApp to help reduce bias in ML models in transactions, credit card approvals and loans
- Host: GitHub
- URL: https://github.com/jai0212/cash-app-bias-busters
- Owner: Jai0212
- Created: 2024-09-29T21:05:51.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-11T22:30:25.000Z (7 days ago)
- Last Synced: 2024-11-11T23:27:36.058Z (7 days ago)
- Language: JavaScript
- Homepage: https://cash-app-bias-busters.netlify.app
- Size: 11.5 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Bias Detection and Visualization Tool
[](https://app.netlify.com/sites/cash-app-bias-busters/deploys)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/)
[](https://flask.palletsprojects.com/)
[](https://reactjs.org/)
[](https://www.mysql.com/)
# Bias Detection and Visualization Tool
As part of the Technology Leadership Initiative, we developed a bias detection and visualization tool in partnership with Cash App. This tool is designed as a dashboard that enables Cash App machine learning (ML) engineers to:
- Import their transaction data and transaction approval models.
- Select core demographic categories like gender, age, and race.
## Index
1. [Title](#bias-detection-and-visualization-tool)
2. [Description](#short-description)
3. [Motivation](#motivation)
4. [How to Run](#how-to-runuse)
5. [Features](#features)
6. [Technical Implementation Details](#technical-implementation-details)
7. [Acknowledgements/Credits](#acknowledgementscredits)
8. [License](#license)
## Motivation
With the increasing integration of AI systems in sensitive applications like financial transactions, ensuring fairness and mitigating biases is crucial. This project addresses the need for tools that allow ML practitioners to identify and visualize potential biases in their models, thus enabling informed decisions to improve fairness in AI.
Our backend analyzes the bias in the selected demographic categories using the Fairlearn library. The results are visualized in intuitive, easy-to-understand graphs created using Chart.js on the frontend.
## Features
- **Login/Sign-up and Account Management**: Secure user authentication and account management to ensure data privacy and personalized access.
- Bias Analysis: Detect bias in transaction approval models for selected demographic categories using the Fairlearn library.
- Interactive Dashboard: User-friendly interface to import data and select demographic groups.
- **User Data Storage**: All user-uploaded data is securely stored in a MySQL database hosted on Aiven.
- Visualizations: Generate intuitive graphs and charts using Chart.js for detailed insights.
- Model Comparison: Easily compare fairness metrics across different models.
## How to run/use
If you want to use the project, simply visit the publicly hosted website: [https://cash-app-bias-busters.netlify.app/](https://cash-app-bias-busters.netlify.app/)
.
If you'd like to run it locally instead, follow these steps:
### Backend:
```
pip install -r requirements.txt
cd backend
python3 -m app.controllers.app
```
### Frontend
```
cd frontend
npm run dev
```
## Technical Implementation Details
- Backend: Built using Python with the Flask framework, the backend handles data processing and bias detection. Hosted on Render.
- Frontend: Built using React and Vite, the frontend is responsible for the interactive dashboard and visualizations. Hosted on Netlify.
- Database: We use MySQL to store all the data uploaded by users. The database is hosted on Aiven.
- Fairlearn: This Python library is used for detecting and assessing fairness in ML models.
- Chart.js: Used to render dynamic and interactive graphs and charts to visualize bias metrics.
## Acknowledgements/Credits
1. This project makes use of the Fairlearn library for bias detection and fairness assessments.
```
@misc{weerts2023fairlearn,
title={Fairlearn: Assessing and Improving Fairness of AI Systems},
author={Hilde Weerts and Miroslav Dudík and Richard Edgar and Adrin Jalali and Roman Lutz and Michael Madaio},
journal={Journal of Machine Learning Research},
year={2023},
volume={24},
number={257},
pages={1--8},
url={http://jmlr.org/papers/v24/23-0389.html}
}
```
2. Special thanks to [https://cash.app/](Cash App) for their collaboration and support in this initiative.
## License
This project is licensed under the MIT License. You are free to use, modify, and distribute this project as per the license terms.
### MIT License
This project is licensed under the MIT License. You are free to use, modify, and distribute this project as per the license terms.
See the [LICENSE.txt](/LICENSE.txt) file for more details.