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.

该研究提出了一种视觉分析系统（ESCAPE），通过人类参与者的观测和量化，以及减轻偏见的解决方案（debias 方法），在避免人工智能盲点（AI blindspots）和系统性错误方面具有较高的实用性。