AutoXAI

Abstract

A large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed in recent years. Recently, thanks to new XAI evaluation metrics, it has become possible to compare these XAI solutions. However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially if a user has specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specified XAI evaluation metrics while considering the user's context (dataset, machine learning model, XAI needs and constraints). It adapts approaches from context-aware recommender systems on one side and strategies of optimization and evaluation from AutoML (Automated Machine Learning) on the other. Through two use cases, we show that AutoXAI recommends XAI solutions adapted to the user's needs with the best hyperparameters matching the user's constraints. CCS CONCEPTS• Computing methodologies → Machine learning.

Uncertainty in XAI: Human Perception and Modeling Approaches

“…Two primary paradigms dominate the discourse: fully automated evaluation and human-in-the-loop (HIL) approaches. Proponents of the former approach advocate for quantitative metrics to assess explanations objectively [15,58,59]. However, defining universally applicable metrics that capture the essence of explanations and allow comparisons across diverse XAI methods proves challenging.…”