  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
