BeXAI is a flexible benchmark suite to evaluate AI models and explainers
with explainability metrics using various datasets.

BeXAI has the following features:

• State-of-the-art AI models in python

• The latest explainers to interpret model behavior

• A ready-to-use python virtual environment for easy start

• A modular code organization, making it easier to bring your own models, explainers, and datasets

Motivation

Introduction of sophisticated models such as neural networks and ensembling methods have led to better accuracies which in-turn has led to building great AI-enabled products. Although they provide very good performance, the internal behavior of these models is difficult to understand (Black box). This property has hindered their scaled deployment in sensitive sectors. As a result, there is an increasing interest and work going to make machine learning models more explainable. However, there is no standard to measure these new explainability improvement against prior works. BexAI is a benchmark suite that tries to address this issue. In the current implementation, three implementation of model agnostic explainers are compared.

Use Cases