  This study progresses solar flare prediction research by presenting a
full-disk deep-learning model to forecast $\geq$M-class solar flares and
evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb
(beyond $\pm$70$^\circ$) events, showcasing qualitative assessment of post hoc
explanations for the model's predictions, and providing empirical findings from
human-centered quantitative assessments of these explanations. Our model is
trained using hourly full-disk line-of-sight magnetogram images to predict
$\geq$M-class solar flares within the subsequent 24-hour prediction window.
Additionally, we apply the Guided Gradient-weighted Class Activation Mapping
(Guided Grad-CAM) attribution method to interpret our model's predictions and
evaluate the explanations. Our analysis unveils that full-disk solar flare
predictions correspond with active region characteristics. The following points
represent the most important findings of our study: (1) Our deep learning
models achieved an average true skill statistic (TSS) of $\sim$0.51 and a
Heidke skill score (HSS) of $\sim$0.38, exhibiting skill to predict solar
flares where for central locations the average recall is $\sim$0.75 (recall
values for X- and M-class are 0.95 and 0.73 respectively) and for the near-limb
flares the average recall is $\sim$0.52 (recall values for X- and M-class are
0.74 and 0.50 respectively); (2) qualitative examination of the model's
explanations reveals that it discerns and leverages features linked to active
regions in both central and near-limb locations within full-disk magnetograms
to produce respective predictions. In essence, our models grasp the shape and
texture-based properties of flaring active regions, even in proximity to limb
areas -- a novel and essential capability with considerable significance for
operational forecasting systems.
