Big data and machine learning tools have jointly empowered humans in making
data-driven decisions. However, many of them capture empirical associations
that might be spurious due to confounding factors and subgroup heterogeneity.
The famous Simpson's paradox is such a phenomenon where aggregated and
subgroup-level associations contradict with each other, causing cognitive
confusions and difficulty in making adequate interpretations and decisions.
Existing tools provide little insights for humans to locate, reason about, and
prevent pitfalls of spurious association in practice. We propose VISPUR, a
visual analytic system that provides a causal analysis framework and a
human-centric workflow for tackling spurious associations. These include a
CONFOUNDER DASHBOARD, which can automatically identify possible confounding
factors, and a SUBGROUP VIEWER, which allows for the visualization and
comparison of diverse subgroup patterns that likely or potentially result in a
misinterpretation of causality. Additionally, we propose a REASONING
STORYBOARD, which uses a flow-based approach to illustrate paradoxical
phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure
accountable decision-making. Through an expert interview and a controlled user
experiment, our qualitative and quantitative results demonstrate that the
proposed "de-paradox" workflow and the designed visual analytic system are
effective in helping human users to identify and understand spurious
associations, as well as to make accountable causal decisions.

VISPUR 是一个视觉分析系统，通过提供因果分析框架和以人为中心的工作流程，帮助人们识别和理解虚假关联，并做出负责任的因果决策。

VISPUR: 用于识别和解释数据驱动决策中的伪关联的可视辅助工具

VISPUR: Visual Aids for Identifying and Interpreting Spurious  Associations in Data-Driven Decisions

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。