IROF：解释方法的低资源评估度量
IROF: a low resource evaluation metric for explanation methods
摘要：
在医疗保健中采用机器学习取决于所用算法的透明度，因此需要解释方法。然而，尽管越来越多的文献解释神经网络，没有达成共识，如何评估这些解释方法。我们提出了IROF，一种新的方法来评估解释方法，避免了人工评估的需要。与其他最近的工作相比，我们的方法需要几个数量级的计算资源，没有人力投入，使其能够访问较低的资源组和强大的人类偏见。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
