OpenXAI: Towards a Transparent Evaluation of Model Explanations

TL;DR

OpenXAI is an open-source framework that standardizes and automates the benchmarking of post hoc explanation methods for machine learning models, promoting transparency and reproducibility.

Contribution

It introduces a comprehensive, extensible platform with diverse datasets, models, and metrics for evaluating explanation methods across multiple criteria.

Findings

Provides a unified pipeline for explanation evaluation

Enables comparison of explanation methods across datasets and metrics

Supports extensibility for custom methods and metrics

Abstract

While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible open-source framework for evaluating and benchmarking post hoc explanation methods. OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets. OpenXAI is easily extensible, as users can readily evaluate custom explanation methods and incorporate them into our leaderboards. Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods. While the first release of OpenXAI supports only tabular datasets, the explanation methods and metrics that we consider are general enough to be applicable to other data modalities. OpenXAI datasets and models, implementations of state-of-the-art explanation methods and evaluation metrics, are publicly available at this GitHub link.