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

• URL: http://arxiv.org/abs/2312.12102v2
• Date: Wed, 10 Jan 2024 15:22:23 GMT
• Title: I-CEE: Tailoring Explanations of Image Classification Models to User Expertise
• Authors: Yao Rong, Peizhu Qian, Vaibhav Unhelkar, Enkelejda Kasneci
• Abstract summary: We present I-CEE, a framework that provides Image Classification Explanations tailored to User Expertise. I-CEE models the informativeness of the example images to depend on user expertise, resulting in different examples for different users. Experiments with simulated users show that I-CEE improves users' ability to accurately predict the model's decisions.
• Score: 13.293968260458962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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