论文标题
我为什么要选择你？ Autoxai：选择和调整可解释的AI解决方案的框架
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
论文作者
论文摘要
近年来，已经提出了大量XAI（可解释的人工智能）解决方案来解释现有的ML（机器学习）模型或创建可解释的ML模型。最近提出了评估措施，现在可以比较这些XAI解决方案。但是，在所有这些多样性中选择最相关的XAI解决方案仍然是一项繁琐的任务，尤其是在满足特定需求和约束时。在本文中，我们提出了Autoxai，该框架是根据特定的XAI评估指标推荐最佳XAI解决方案及其超参数的框架，同时考虑用户的上下文（数据集，ML模型，XAI需求和约束）。它可以从AutoML（自动化机器学习）的优化和评估策略中适应上下文感知的推荐系统和策略。我们将Autoxai应用于两种用例，并表明它建议使用与用户约束相匹配的最佳超参数适合用户需求的XAI解决方案。
In recent years, a large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed to explain existing ML (Machine Learning) models or to create interpretable ML models. Evaluation measures have recently been proposed and it is now possible to compare these XAI solutions. However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially when meeting specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specific XAI evaluation metrics while considering the user's context (dataset, ML model, XAI needs and constraints). It adapts approaches from context-aware recommender systems and strategies of optimization and evaluation from AutoML (Automated Machine Learning). We apply AutoXAI to two use cases, and show that it recommends XAI solutions adapted to the user's needs with the best hyperparameters matching the user's constraints.
