BeXAI

This is a benchmark suite for selected explainable AI (XAI).

The benchmark suite consists of Target machine learning models that are to be explained. These models are trained across text, image and tabular datasets. Two prominent model-agnostic, post-hoc local explainers are used to generate interpretations of predictions made by the target models: LIME(Local Interpretable Model-agnostic Explanation) and SHAP(SHapley Additive exPlanation). To evaluate the performance of these explainers, fidelity and monotonicity metrics are used. Fidelity measures how well the exlanations match with the model prediction. Monotonicity(consistency) is desirabe characterstic that feature importance based explainers like SHAP and LIME to have. Monotonicity checks if the explanation has this property

Installation

Install the required dependencies in requirement.txt using the following command

pip install -r requirements.txt

Sample Usage

Use the following command to test the performance of SHAP and LIME explainers in interpreting three regression models: Linear Regression, Random Forest Regression and SVM Regression when trained on boston dataset.

python3 main.py --mode regression --dataset boston

or edit the script run.sh and run it as

./run.sh

NB for regression mode the available dataset options are boston, superconductivity and diabetes datasets. And for classification mode, the available dataset options are wine and breast_cancer(for tabular dataset), image and text for image and text datasets resp. Passing different arguments will cause the application throw exceptions.

Target Models

Regression Models:

• Linear Regression

• Random Forest Regression

• Support Vector Machine Regression

Classification Models:

• Logistic Regression

• Random Forest Classification

• Support Vector Machine Classification

• CNN Image Classification

• RNN Text Classification

Datasets:

Tabular Datasets:

For Regression Task

• Boston Housing Dataset-available on Scikit Learn Library

• Superconductivity-available on UCI ML dataset repository Superconductivty Data Data Set

• Diabetes Dataset- available on Scikit Learn Library

For Classification Task

• Wine Dataset-available on Scikit Learn Library

• Breast Cancer Dataset-available on Scikit Learn Library

Image Dataset:

• MNist handwritten digit dataset-available on Keras Library

Text Dataset:

• IMDB -available on Keras Library

Explainers

SHAP: proposed by Lundberg et al, SHAP explainer uses coalition game theory to calculate the contributions of each feature to the prediction.

Paper: Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions” Advances in Neural Information Processing Systems. 2017

LIME: creates explanation by building proxy linear model(like Linear reg and Decision tree) that approximates the target model around the instance of interest and assigning importance of features according to the coefficients of the proxy model

Paper: Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Why should I trust you?: Explaining the predictions of any classifier.” Proceedings of the 22nd

Evaluation metrics

Fidelity: is measured by first by removing each feature by replacing it with its mean value and get prediction for this instance. Then measure the correlation between its importance and the change in prediction.

Monotonicity: is measured by adding features in order of their importance then observe if the prediction value does not decrease with each introduction of features.