Abstract
State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial Intelligence (XAI) life cycle, enabling practitioners to iteratively identify, mitigate, and (re-)evaluate spurious model behavior with a minimal amount of human interaction. In the first step (1), R2R reveals model weaknesses by finding outliers in attributions or through inspection of latent concepts learned by the model. Secondly (2), the responsible artifacts are detected and spatially localized in the input data, which is then leveraged to (3) revise the model behavior. Concretely, we apply the methods of RRR, CDEP and ClArC for model correction, and (4) (re-)evaluate the model's performance and remaining sensitivity towards the artifact. Using two medical benchmark datasets for Melanoma detection and bone age estimation, we apply our R2R framework to VGG, ResNet and EfficientNet architectures and thereby reveal and correct real dataset-intrinsic artifacts, as well as synthetic variants in a controlled setting. Completing the XAI life cycle, we demonstrate multiple R2R iterations to mitigate different biases. Code is available on this https URL.
Abstract (translated)
最先进的机器学习模型常常在训练数据中学习伪相关性。当将这些模型用于重要决策时,例如医疗应用如皮肤癌检测时,这就构成了风险。为了解决这一问题,我们提出了“揭示以更新”(R2R)框架,该框架涵盖了整个可解释人工智能(XAI)生命周期,使用户可以迭代地识别、减轻和(再次)评估伪模型行为,而只需要少量的人类交互。在第一个步骤(1),R2R通过发现归因异常值或检查模型学习的潜在概念来揭示模型的弱点。在第二个步骤(2),负责的元数据被检测并空间定位在输入数据中,然后利用它来(再次)更新模型行为。具体来说,我们应用RRR、CDEP和ClArC方法来进行模型修正,并(再次)评估模型的性能和剩余的对元数据敏感性。使用两个医疗基准数据集来检测和估计 Melanoma 检测和骨龄估计,我们应用我们的 R2R 框架到 VGG、ResNet 和 EfficientNet 架构上,从而揭示和纠正真实的数据源固有元数据,以及在控制环境下的人造变异体。完成 XAI 生命周期后,我们展示了多个 R2R 迭代以减轻不同的偏见。代码在此 https URL 可用。
URL
https://arxiv.org/abs/2303.12641
