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 https://github.com/maxdreyer/Reveal2Revise.

翻译：当前最先进的机器学习模型常学习训练数据中隐藏的虚假相关性，这导致其在高风险决策场景（如皮肤癌检测等医学应用）中部署时存在风险。针对该问题，我们提出"揭示与修正"（R2R）框架，该框架涵盖可解释人工智能（XAI）全生命周期，支持实践者以最少人工干预迭代式地识别、缓解和（重新）评估模型的虚假行为。第一步（1）：R2R通过发现归因异常值或检测模型学习到的隐层概念来揭示模型缺陷；第二步（2）：识别输入数据中的因果伪影并定位其空间分布；进而（3）修正模型行为——具体采用RRR、CDEP和ClArC等方法实现模型校正；最终（4）（重新）评估模型性能及对伪影的残留敏感性。基于黑色素瘤检测和骨龄评估两个医学基准数据集，我们将R2R框架应用于VGG、ResNet和EfficientNet架构，在受控条件下揭示并校正了真实数据集固有伪影及合成变体伪影。通过完成XAI全生命周期，我们展示了多轮R2R迭代以缓解不同偏置的可行性。代码已开源：https://github.com/maxdreyer/Reveal2Revise