论文标题
利他主义者：通过当地解释预测模型的论证解释
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models
论文作者
论文摘要
可解释的AI是一个新兴领域，提供解决方案，以获取对自动化系统理由的见解。它通过提出解决关键道德和社会问题的方法来放在AI地图上。最终用户通常无法理解现有的解释技术。缺乏评估和选择标准也使最终用户很难选择最合适的技术。在这项研究中，我们将基于逻辑的论点与可解释的机器学习结合在一起，引入了一种初步的元解释方法，该方法识别了特征重要性的面向意义解释的真实部分。除了用作元解释技术之外，这种方法还可以用作多种功能重要性技术的评估或选择工具。实验强烈表明，多种解释技术的合奏产生了更真实的解释。
Explainable AI is an emerging field providing solutions for acquiring insights into automated systems' rationale. It has been put on the AI map by suggesting ways to tackle key ethical and societal issues. Existing explanation techniques are often not comprehensible to the end user. Lack of evaluation and selection criteria also makes it difficult for the end user to choose the most suitable technique. In this study, we combine logic-based argumentation with Interpretable Machine Learning, introducing a preliminary meta-explanation methodology that identifies the truthful parts of feature importance oriented interpretations. This approach, in addition to being used as a meta-explanation technique, can be used as an evaluation or selection tool for multiple feature importance techniques. Experimentation strongly indicates that an ensemble of multiple interpretation techniques yields considerably more truthful explanations.
