Atmospheric surface fronts (SFs) strongly influence midlatitude weather through storms, precipitation, and abrupt temperature changes, motivating reliable detection. Here, we reproduce a U-Net, a type of artificial neural network, to detect SFs in ERA5 reanalysis data with labelled datasets over North America and Europe. The goal of this study is to apply Layer-wise Relevance Propagation (LRP) to interpret the learned patterns. To reduce computational cost, we introduce a, compared to previous studies, reduced U-Net variant and a reduced input set of six variables - temperature, specific humidity, and horizontal wind - without reducing detection quality. Relevance analysis shows that U-Net focuses partly on physically meaningful regions near detected SFs, consistent with rule-based methods, but also noisy patterns and spurious correlations far from the detected fronts. Moreover, U-Net employs two nearly inverted relevance patterns for different parts of a front, which we see as not physically plausible.</p> <p>Using a simplified synthetic dataset of a 1-D field with SF labels placed at the minimum of the second derivative, we can link the pattern to the front position relative to the frontal zone. Increasing the label resolution enables the U-Net to learn a more plausible pattern without the split in the synthetic case.</p> <p>When evaluated over a larger region (Pacific to Europe, equator to Arctic), U-Net performs well in midlatitudes but over-detects SFs in the Tropics and Arctic, for example regularly detecting SFs within tropical cyclones. Together with the partially implausible relevance patterns, this indicates that ANN-based SF detection should be used with care outside the training regions. Retraining on the larger domain improves detection skill and the plausibility of relevance patterns, but the split pattern persists. Overall, the results highlight the value of explainable AI for identifying physically implausible shortcuts and the need for a global SF dataset to enable reliable detection worldwide.