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

翻译：有效解释黑盒机器学习模型的决策对于依赖这些模型的AI系统的负责任部署至关重要。认识到其重要性后，可解释人工智能领域提供了多种生成这些解释的技术。然而，在这一不断增长的研究领域中，对用户（被解释者）的关注相对较少，大多数XAI技术生成的是"一刀切"的解释。为弥合这一差距并进一步迈向以人为中心的XAI，我们提出了I-CEE框架，该框架提供面向用户专业知识的图像分类解释。基于现有研究，I-CEE通过向用户提供信息量丰富的训练数据子集（即示例图像）、相应的局部解释及模型决策来解释图像分类模型的决策。与先前工作不同，I-CEE将示例图像的信息量建模为依赖于用户专业知识的函数，从而为不同用户生成不同示例。我们提出，通过根据用户专业知识定制示例集，I-CEE能更好地促进用户对模型的理解和可模拟性。为评估该方法，我们在多个数据集上开展了详细的仿真实验和人类受试者实验（N=100）。仿真用户实验表明，与基线方法相比，I-CEE在提升用户准确预测模型决策（可模拟性）方面取得了有前景的初步结果。人类受试者实验证明，我们的方法显著提高了用户可模拟性准确性，凸显了以人为中心XAI的重要性。