Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
src
 
 
 
 
 
 
 
 
 
 
 
 
 
 

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

FairVis is a visual analytics system that allows users to audit their classification models for intersectional bias. Users can generate subgroups of their data and investigate if a model is underperforming for certain populations.

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng (Polo) Chau IEEE Conference on Visual Analytics Science and Technology (VAST). 2019.

teaser figure

In this example we show how FairVis can be used on the COMPAS dataset to find significant disparities in false positive rates between African American and Caucasian defendants that are not supported by base rates. The primary components of the system are the following:

A. View distributions of the dataset's features and generate subgroups.

B. Visualize subgroup performance in relation to selected metrics.

C. Compare selected subgroups and view details.

D. Find suggested underperforming subgroups and similar groups.

For more details about the system and its use cases, see the IEEE VAST paper.

Installation

Clone the repository:

git clone https://github.com/poloclub/FairVis.git

Then initialize the React project by running

npm install

Usage

Run the server with

npm start

Adding a new dataset

  1. Run a model on your data and and create a new file with the last two columns being the output class (between 0-1) of the model and the ground truth labels (0 or 1). Note that only binary classification is currently supported. Examples of models in Jupyter Notebook format can be found in ./models.

  2. Run the preprocess.py script on your classified data, e.g. python3 preprocess.py my-data-with-classes.csv. Additional options for the helper function can be found using python3 preprocess.py -h.

  3. Save the processed file to ./src/data/.

  4. Import the file in the src/components/Welcome.js component.

  5. Add a new row to the table in Welcome.js around line 140 in the form of the other datsets.

Researchers

Name Affiliation
Ángel Alexander Cabrera Georgia Tech
Will Epperson Georgia Tech
Fred Hohman Georgia Tech
Minsuk Kahng Georgia Tech
Jamie Morgenstern Georgia Tech
Duen Horng (Polo) Chau Georgia Tech

Citation

@inproceedings{cabrera2019fairvis,
  title={FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning},
  author={Cabrera, {'A}ngel Alexander and Epperson, Will and Hohman, Fred and Kahng, Minsuk and Morgenstern, Jamie and Chau, Duen Horng},
  booktitle={2019 IEEE Conference on Visual Analytics Science and Technology (VAST)},
  pages={46-56},
  year={2019},
  publisher={IEEE},
  doi={10.1109/VAST47406.2019.8986948},
  url={https://cabreraalex.com/#/paper/fairvis}
}

License

MIT License. See LICENSE.md.

About

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

Resources

License

Releases

No releases published

Packages

No packages published