Via thousands of papers in Explainable AI (XAI), attention maps
\cite{vaswani2017attention} and feature attribution maps \cite{bansal2020sam}
have been established as a common means for explaining the input features that
are important to AI's decisions. It is an interesting but unexplored question
whether allowing users to edit the importance scores of input features at test
time would improve the human-AI team's accuracy on downstream tasks. In this
paper, we address this question by taking CHM-Corr, a state-of-the-art,
ante-hoc explanation method \cite{taesiri2022visual} that first predicts
patch-wise correspondences between the input and the training-set images, and
then uses them to make classification decisions. We build an interactive
interface on top of CHM-Corr, enabling users to directly edit the initial
feature attribution map provided by CHM-Corr. Via our CHM-Corr++ interface,
users gain insights into if, when, and how the model changes its outputs,
enhancing understanding beyond static explanations. Our user study with 18
machine learning researchers who performed $\sim$1,400 decisions shows that our
interactive approach does not improve user accuracy on CUB-200 bird image
classification over static explanations. This challenges the belief that
interactivity inherently boosts XAI
effectiveness~\cite{sokol2020one,sun2022exploring,shen2024towards,singh2024rethinking,mindlin2024beyond,lakkaraju2022rethinking,cheng2019explaining,liu2021understanding}
and raises needs for future research. Our work contributes to the field by
open-sourcing an interactive tool for manipulating model attention, and it lays
the groundwork for future research to enable effective human-AI interaction in
computer vision. We release code and data on
\href{this https URL}{github}. Our
interface are available \href{this http URL}{here}.

通过介绍可解释的人工智能（Explainable AI）领域的研究，表明关注图和特征归因图已经成为解释人工智能决策中重要输入特征的常见方式。本研究基于 CHM-Corr 方法，通过构建一个交互界面，使用户能够直接编辑 CHM-Corr 提供的初始特征归因图，从而提高对模型输出的理解，并对交互方法进行了用户研究，结果表明交互方法并未提高用户在基于鸟类图像分类的任务上的准确性，进一步需要深入研究提高人工智能与人类的有效互动。