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.

翻译：分类模型学习泛化数据样本与其目标类别之间的关联。然而，研究人员日益发现机器学习实践容易导致AI应用中出现系统性错误，这一现象被称为AI盲点。当模型使用训练样本（如猫/狗分类）进行训练时，若其中重要模式（如黑猫）缺失，或外围/非期望模式（如带草地背景的狗）误导某一类别，便会引发此类盲点。即使更复杂的技术也无法保证能够捕获、推理并防止这些虚假关联。在本工作中，我们提出ESCAPE——一种支持人机协同工作流程以对抗系统性错误的视觉分析系统。该系统允许人类用户轻松检查虚假关联，促进用户自发识别与误分类相关的概念，并评估可减少偏差关联的缓解策略。我们还提出两种统计方法：相对概念关联以更量化地衡量概念与实例之间的关联性，以及去偏方法以缓解虚假关联。通过包括定量实验、使用场景、专家访谈和受控用户实验在内的广泛评估，我们展示了所提出的ESCAPE系统与统计测度的实用性。