In recent years, considerable work has been devoted to explaining predictive,
deep learning-based models, and in turn how to evaluate explanations. An
important class of evaluation methods are ones that are human-centered, which
typically require the communication of explanations through visualizations. And
while visualization plays a critical role in perceiving and understanding model
explanations, how visualization design impacts human perception of explanations
remains poorly understood. In this work, we study the graphical perception of
model explanations, specifically, saliency-based explanations for visual
recognition models. We propose an experimental design to investigate how human
perception is influenced by visualization design, wherein we study the task of
alignment assessment, or whether a saliency map aligns with an object in an
image. Our findings show that factors related to visualization design
decisions, the type of alignment, and qualities of the saliency map all play
important roles in how humans perceive saliency-based visual explanations.

通过实验设计研究可视化设计决策、对齐类型和显著性地图质量对人类感知显著性视觉解释的影响。

基于显著性模型解释的图形感知

Graphical Perception of Saliency-based Model Explanations

This study presents insights from interviews with nineteen Knowledge Graph
(KG) practitioners who work in both enterprise and academic settings on a wide
variety of use cases. Through this study, we identify critical challenges
experienced by KG practitioners when creating, exploring, and analyzing KGs
that could be alleviated through visualization design. Our findings reveal
three major personas among KG practitioners - KG Builders, Analysts, and
Consumers - each of whom have their own distinct expertise and needs. We
discover that KG Builders would benefit from schema enforcers, while KG
Analysts need customizable query builders that provide interim query results.
For KG Consumers, we identify a lack of efficacy for node-link diagrams, and
the need for tailored domain-specific visualizations to promote KG adoption and
comprehension. Lastly, we find that implementing KGs effectively in practice
requires both technical and social solutions that are not addressed with
current tools, technologies, and collaborative workflows. From the analysis of
our interviews, we distill several visualization research directions to improve
KG usability, including knowledge cards that balance digestibility and
discoverability, timeline views to track temporal changes, interfaces that
support organic discovery, and semantic explanations for AI and machine
learning predictions.

该研究通过对 19 名企业和学术背景下的知识图谱从业者的访谈，识别出知识图谱构建、探索和分析中出现的关键挑战，并探讨了可视化设计的应用，从而发现了三种不同的用户类型，以及每种类型在构建和使用知识图谱中的专业和需求。最后，从访谈分析中提取出几个可视化研究方向，以提高知识图谱的可用性。

实践中知识图谱的用户、挑战和可视化需求刻画

Characterizing the Users, Challenges, and Visualization Needs of  Knowledge Graphs in Practice

Data visualization should be accessible for all analysts with data, not just
the few with technical expertise. Visualization recommender systems aim to
lower the barrier to exploring basic visualizations by automatically generating
results for analysts to search and select, rather than manually specify. Here,
we demonstrate a novel machine learning-based approach to visualization
recommendation that learns visualization design choices from a large corpus of
datasets and associated visualizations. First, we identify five key design
choices made by analysts while creating visualizations, such as selecting a
visualization type and choosing to encode a column along the X- or Y-axis. We
train models to predict these design choices using one million
dataset-visualization pairs collected from a popular online visualization
platform. Neural networks predict these design choices with high accuracy
compared to baseline models. We report and interpret feature importances from
one of these baseline models. To evaluate the generalizability and uncertainty
of our approach, we benchmark with a crowdsourced test set, and show that the
performance of our model is comparable to human performance when predicting
consensus visualization type, and exceeds that of other ML-based systems.

本研究介绍了一种基于机器学习的数据可视化推荐方法，通过从大量数据集和相关可视化图中训练出网络来预测设计选择，从而降低探索基本可视化的难度.