ESCAPE：通过交互式视觉分析消除机器盲点的系统误差
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis
摘要：
分类模型学习概括数据样本与其目标类之间的关联。然而，研究人员越来越多地观察到，机器学习实践很容易导致AI应用中的系统性错误，这种现象被称为AI盲点。当用训练样本训练模型时（例如，猫/狗分类），其中重要的模式（例如，黑猫）是缺失的或外围/不期望的图案（例如，有草背景的狗）会误导某个阶层。即使是更复杂的技术也不能保证捕获、推理和防止虚假的关联。在这项工作中，我们提出了ESCAPE，一个可视化的分析系统，促进了人在循环的工作流程，以对抗系统性错误。通过允许人类用户容易地检查虚假关联，该系统便于用户自发地识别与错误分类相关联的概念，并评估可以减少有偏见的关联的缓解策略。我们还提出了两种统计方法，相对概念关联，以更好地量化概念和实例之间的关联，和debias方法，以减轻虚假的关联。我们通过广泛的评估，包括定量实验，使用场景，专家访谈和控制用户实验，证明了我们提出的ESCAPE系统和统计措施的实用性。
英文摘要：
Classification models learn to generalize the associations between data samples and their target classes. However, researchers have increasingly observed that machine learning practice easily leads to systematic errors in AI applications, a phenomenon referred to as AI blindspots. Such blindspots arise when a model is trained with training samples (e.g., cat/dog classification) where important patterns (e.g., black cats) are missing or periphery/undesirable patterns (e.g., dogs with grass background) are misleading towards a certain class. Even more sophisticated techniques cannot guarantee to capture, reason about, and prevent the spurious associations. In this work, we propose ESCAPE, a visual analytic system that promotes a human-in-the-loop workflow for countering systematic errors. By allowing human users to easily inspect spurious associations, the system facilitates users to spontaneously recognize concepts associated misclassifications and evaluate mitigation strategies that can reduce biased associations. We also propose two statistical approaches, relative concept association to better quantify the associations between a concept and instances, and debias method to mitigate spurious associations. We demonstrate the utility of our proposed ESCAPE system and statistical measures through extensive evaluation including quantitative experiments, usage scenarios, expert interviews, and controlled user experiments.
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。
