Effectively explaining decisions of black-box machine learning models is
critical to responsible deployment of AI systems that rely on them. Recognizing
their importance, the field of explainable AI (XAI) provides several techniques
to generate these explanations. Yet, there is relatively little emphasis on the
user (the explainee) in this growing body of work and most XAI techniques
generate "one-size-fits-all" explanations. To bridge this gap and achieve a
step closer towards human-centered XAI, we present I-CEE, a framework that
provides Image Classification Explanations tailored to User Expertise. Informed
by existing work, I-CEE explains the decisions of image classification models
by providing the user with an informative subset of training data (i.e.,
example images), corresponding local explanations, and model decisions.
However, unlike prior work, I-CEE models the informativeness of the example
images to depend on user expertise, resulting in different examples for
different users. We posit that by tailoring the example set to user expertise,
I-CEE can better facilitate users' understanding and simulatability of the
model. To evaluate our approach, we conduct detailed experiments in both
simulation and with human participants (N = 100) on multiple datasets.
Experiments with simulated users show that I-CEE improves users' ability to
accurately predict the model's decisions (simulatability) compared to
baselines, providing promising preliminary results. Experiments with human
participants demonstrate that our method significantly improves user
simulatability accuracy, highlighting the importance of human-centered XAI

基于用户专业知识的人性化可解释人工智能（HCI-XAI）框架 I-CEE，通过提供信息丰富的训练数据子集、相应的局部解释和模型决策，解释了图像分类模型的决策，以提高用户对模型的理解和模拟能力。实验结果表明，该方法在提高用户模拟能力准确性方面取得了显著改善，突出了人性化可解释人工智能的重要性。

I-CEE: 图像分类模型根据用户专业知识定制解释

I-CEE: Tailoring Explanations of Image Classifications Models to User  Expertise

The boundaries of existing explainable artificial intelligence (XAI)
algorithms are confined to problems grounded in technical users' demand for
explainability. This research paradigm disproportionately ignores the larger
group of non-technical end users, who have a much higher demand for AI
explanations in diverse explanation goals, such as making safer and better
decisions and improving users' predicted outcomes. Lacking
explainability-focused functional support for end users may hinder the safe and
accountable use of AI in high-stakes domains, such as healthcare, criminal
justice, finance, and autonomous driving systems. Built upon prior human factor
analysis on end users' requirements for XAI, we identify and model four novel
XAI technical problems covering the full spectrum from design to the evaluation
of XAI algorithms, including edge-case-based reasoning, customizable
counterfactual explanation, collapsible decision tree, and the verifiability
metric to evaluate XAI utility. Based on these newly-identified research
problems, we also discuss open problems in the technical development of
user-centered XAI to inspire future research. Our work bridges human-centered
XAI with the technical XAI community, and calls for a new research paradigm on
the technical development of user-centered XAI for the responsible use of AI in
critical tasks.

本研究介绍了四个新的可解释人工智能技术问题，这些问题围绕设计和评估算法，并呼吁建立基于人类需求的可解释人工智能技术开发研究范式，以保证人工智能在关键任务中的负责任使用。