Decomposing decisions: causal concept explanations for deep learning models

Engineering notes

Key topics: autonomous driving, semantic segmentation, deployment. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The deployment of deep learning models in high-stakes applications such as autonomous driving is critically important, yet their black-box nature remains a fundamental barrier to trust and accountability. Existing explainability methods typically produce ambiguous, pixel-based heatmaps that capture correlation rather than establishing a causal link between high-level, human-interpretable concepts and model outputs. This paper introduces the Causal Concept Decomposer (CCD), a three-stage framework for concept-driven causal explanation of object detectors. CCD first employs semantic segmentation to isolate the target object, then applies Non-negative Matrix Factorization to discover constituent semantic parts, and finally uses Sobol sensitivity analysis to quantify the causal influence of each part on the detector’s decision. Evaluated on the MS COCO dataset, CCD produces explanations that are both visually coherent and quantitatively more faithful than existing approaches, achieving a Deletion AUC of 0.11 and an Insertion AUC of 0.91. By moving beyond correlational attribution towards principled causal analysis, this work represents an important step towards more trustworthy and reliable AI systems.

Links and sources

Comments