Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large language models to generate global, textual explanations of visual classifiers. DEXTER operates by optimizing text prompts to synthesize class-conditional images that strongly activate a target classifier. These synthetic samples are then used to elicit detailed natural language reports that describe class-specific decision patterns and biases. Unlike prior work, DEXTER enables natural language explanation about a classifier's decision process without access to training data or groundtruth labels. We demonstrate DEXTER's flexibility across three tasks--activation maximization, slice discovery and debiasing, and bias explanation--each illustrating its ability to uncover the internal mechanisms of visual classifiers. Quantitative and qualitative evaluations, including a user study, show that DEXTER produces accurate, interpretable outputs. Experiments on ImageNet, Waterbirds, CelebA, and FairFaces confirm that DEXTER outperforms existing approaches in global model explanation and class-level bias reporting.

The detailed architecture for VanillaNet with 7-13 layers can be found in Table 1, where each convolutional layer is followed with an activation function. For the VanillaNet-13-1.5, the number of channels are multiplied with 1.5. For classification on ImageNet, we train the VanillaNets for 300 epochs utilizing the cosine learning rate decay [5]. The λis linearly decayed from 1 to 0 on epoch 0 and 100, respectively. The training details can be fould in Table 2.

LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose a feature factorization activation map (FFAM) to generate high-quality visual explanations for 3D detectors. FFAM employs non-negative matrix factorization to generate concept activation maps and subsequently aggregates these maps to obtain a global visual explanation. To achieve object-specific visual explanations, we refine the global visual explanation using the feature gradient of a target object. Additionally, we introduce a voxel upsampling strategy to align the scale between the activation map and input point cloud. We qualitatively and quantitatively analyze FFAM with multiple detectors on several datasets. Experimental results validate the high-quality visual explanations produced by FFAM.

We qualitatively and quantitatively analyze FFAM with multiple detectors on several datasets. Experimental results validate the high-quality visual explanations produced by FFAM.

InEXPAND,weaugmenteachhuman evaluated state to 5 states. To verify 5issufficient, we also experimented with the numbers of augmentations required in each state to get the best performance. AGIL [50] was designed to utilize saliency map collected via human gaze. The network architectures are shown in Figure 1. Hence, we view the output of attention network as the prediction of whether a pixel should be included in a human annotated boundingbox.

Human explanation (e.g.,interms offeature importance) hasbeenrecently used to extend the communication channel between human and agent in interactive machinelearning.