This is a repository that contains code for DSC180B section B06's Q2 Project: Explainable AI.
"build-script": "zhw005/dsc180b-project"
In our project, we will be focusing on using different techniques from causal inferences and explainable AI to interpret various machine learning models across various domains. In particular, we are interested in three domains - healthcare, banking, and the housing market. Within each domain, we are going to train several machine learning models first:XGBoost, LightGBM, TabNet, and SVM. And we have four goals in general:
- Explaining black-box models both globally and locally with various XAI methods;
- Assessing the fairness of each learning algorithm with regard to different sensitive attributes;
- Explaining False Negative and False Positive predictions using Causal Inference;
- Generating recourse for individuals - a set of minimal actions to change the prediction of those black-box models.
| target | config | experiment |
|---|---|---|
| airbnb_features | 'config/FeatureEng-params-airbnb.json' | Do feature engineering for airbnb dataset |
| loan_features | 'config/FeatureEng-params-loan.json' | Do feature engineering for loan dataset |
| diabetes_features | 'config/FeatureEng-params-diabetes.json' | Do feature engineering for diabetes dataset |
| fairness | 'config/Fairness-example.json' | Do fairness evaluation |
| FN_FP | 'config/FN_FP-example.json' | Do False Negative and False Positive explanation |
| model_explanations | 'config/Model_Explanations_Example_loan.json' | Do model explanations - loan data example |
| recourse | 'config/Recourse-example.json' | Generate recourse explanation - loan data example |