An interactive tool to help everyday users discover actionable strategies to obtain desired AI decisions.

🚀 Live Demo	📺 Demo Video	📖 Research Paper

GAM Coach is an interactive tool that helps everyday users understand and alter machine learning models' decisions. It shows users diverse ways to change their input data so that they can get a different output from the model, and it allows users to customize these suggestions through a few clicks! For example, if a user wants to get approved for a loan, GAM Coach may suggest they increase their credit score. If the user finds it difficult to increase their credit score, GAM Coach will search for alternative strategies, such as lowering the loan amount.

For a live demo, visit: https://poloclub.github.io/gam-coach/

Clone or download this repository:

git clone [email protected]:poloclub/gam-coach.git

# use --depth if you don't want to download the whole commit history
git clone --depth 1 [email protected]:poloclub/gam-coach.git

Install the dependencies:

cd gamcoach-ui
npm install

Then run GAM Coach:

npm run dev

Navigate to localhost:5005. You should see GAM Coach running in your browser :)

You can also use GAM Coach as a Python library to generate customizable counterfactual examples for generalized additive models (GAMs). You can install GAM Coach through pip.

Then, you only need one function call to generate customizable counterfactual examples for your GAM models!

import gamcoach as coach

# First create a GAM Coach object
# It requires to provide the training data, so it can generate better CFs based
# on the data distribution
my_coach = coach.GAMCoach(ebm, x_train)

cfs = my_coach.generate_cfs(
    cur_example,
    total_cfs=3,
    # List of features that the CFs can change
    features_to_vary=['loan_amnt', 'term', 'emp_length', 'home_ownership',
                      'annual_inc', 'purpose', 'dti', 'open_acc', 'revol_bal',
                      'revol_util', 'total_acc', 'application_type', 'mort_acc',
                      'fico_score'],
    # Some continuous features need to have integer values in practice
    continuous_integer_features=['open_acc', 'total_acc', 'mort_acc', 'fico_score']
)

For more details and examples, read our documentation.

pip install gamcoach

In the demo page, we provide five demos with the most commonly used datasets in the algorithmic recourse literature. You can easily set up a GAM Coach for your own GAM model (with only one function call). See this documentation page for details.

You can also check out this example notebook to see how to extract GAM model weights for GAM Coach.

GAM Coach is a result of a collaboration between ML and visualization researchers from Georgia Tech and Microsoft Research. GAM Coach is created by Jay Wang, Jenn Wortman Vaughan, Rich Caruana, and Polo Chau.

To learn more about GAM Coach, please read our research paper (published at CHI 2023).

@article{wangGAMCoachInteractive2023,
  title = {{{GAM Coach}}: {{Towards Interactive}} and {{User-centered Algorithmic Recourse}}},
  author = {Wang, Zijie J. and Vaughan, Jennifer Wortman and Caruana, Rich and Chau, Duen Horng},
  year = {2023},
  doi = {10.1145/3544548.3580816}
  journal = {arXiv:2302.14165},
  url = {https://arxiv.org/abs/2302.14165},
  archiveprefix = {arXiv}
}

The software is available under the MIT License.

If you have any questions, feel free to open an issue or contact Jay Wang.