论文标题
IROF：解释方法的低资源评估指标
IROF: a low resource evaluation metric for explanation methods
论文作者
论文摘要
医疗保健中的机器学习取决于使用算法的透明度，因此需要进行解释方法。但是，尽管越来越多地解释了神经网络，但如何评估这些解释方法尚未达成共识。我们提出了IROF，这是一种评估解释方法的新方法，该方法规避了对手动评估的需求。与最近的其他工作相比，我们的方法需要少几个数量级的计算资源，而没有人类的投入，这使得资源群体可以使用，并且对人类的偏见有牢固。
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing literature on explaining neural networks, no consensus has been reached on how to evaluate those explanation methods. We propose IROF, a new approach to evaluating explanation methods that circumvents the need for manual evaluation. Compared to other recent work, our approach requires several orders of magnitude less computational resources and no human input, making it accessible to lower resource groups and robust to human bias.
