## 5 Experimental Framework and Results
### Limitations of Standard Analysis and Visualization Techniques in Explainable AI  
Evaluating CARAG's integration of local and global perspectives in post-hoc explanations requires more than standard metrics and visualizations, which often fall short of capturing nuanced thematic and contextual relevance. Metrics like precision, recall, F1, MRR, and MAP measure retrieval performance but do not assess thematic alignment, a critical element in our framework. Similarly, overall accuracy and F1 scores capture binary prediction accuracy without addressing the thematic coherence of explanations. Moreover, standard explainability metrics, such as fidelity, interpretability scores, and sufficiency,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{7}{c}{**(b)** Climate Theme**} \\ \hline
**Index** & **RAG (PCA)** & **CARAG (PCA)** & **Diff (PCA)** & **RAG (t-SNE)** & **CARAG (t-SNE)** & **Diff (t-SNE)** \\ \hline
0 & 0.0590 & 0.6626 & 0.6035 & 37.3316 & 37.8603 & 0.5287 \\ \hline
1 & 0.2180 & 0.1234 & \(-\)0.0946 & 37.5164 & 37.2024 & \(-\)0.3139 \\ \hline
2 & 0.0531 & 0.1442 & 0.0911 & 37.2505 & 37.2061 & \(-\)0.0443 \\ \hline
3 & 0.1518 & 0.2534 & 0.1016 & 37.3610 & 37.5232 & 0.1623 \\ \hline
4 & 0.1551 & 0.1397 & \(-\)0.0154 & 37.1891 & 37.1950 & 0.0060 \\ \hline
5 & 0.1368 & 0.0855 & \(-\)0.0512 & 37.4192 & 37.2654 & \(-\)0.1538 \\ \hline
6 & 0.2749 & 0.1878 & \(-\)0.0871 & 37.5750 & 37.1636 & \(-\)0.4115 \\ \hline
7 & 0.3473 & 0.1930 & \(-\)0.1543 & 37.5689 & 37.1082 & \(-\)0.4607 \\ \hline
8 & 0.1461 & 0.1789 & 0.0328 & 37.1552 & 37.1223 & \(-\)0.0329 \\ \hline
9 & 0.5143 & 0.1252 & \(-\)0.3891 & 37.8044 & 37.4247 & \(-\)0.3797 \\ \hline \multicolumn{7}{c}{_Average_} & **0.2056** & **0.2094** & **0.0037** & **37.4171** & **37.3071** & \(-\)**0.1100** \\ \hline \multicolumn{7}{c}{**(c)** Electric Vehicles Theme**} \\ \hline
**Index** & **RAG (PCA)** & **CARAG (PCA)** & **Diff (PCA)** & **RAG (t-SNE)** & **CARAG (t-SNE)** & **Diff (t-SNE)** \\ \hline
0 & 0.1989 & 0.1155 & \(-\)0.0834 & 76.1226 & 76.1748 & 0.0522 \\
1 & 0.6796 & 0.1405 & \(-\)0.5391 & 76.9621 & 76.4237 & \(-\)0.5385 \\ \hline
2 & 0.4984 & 0.1093 & \(-\)0.3891 &  
typically offer insights at the individual explanation level, lacking the layered depth needed for complex thematic datasets. For instance, when examining the CARAG explanation in Figure 7, which emphasizes a rich thematic alignment by connecting climate change with economic impacts and government policies, it is clear that traditional metrics would not adequately capture this depth of thematic integration. Additionally, even similarity measures struggle here, as the CARAG-generated explanation provides context that aligns with thematic patterns beyond surface-level similarity, contrasting with the simpler human explanation in Figure 7, which lacks this layered thematic framing. Standard visualization techniques, such as box plots, provide a limited view of CARAG's thematic alignment by reducing it to a numeric similarity measure. For example, Figure 9(a) shows a box plot of global coverage scores, where cosine similarity scores between CARAG's explanations and dataset vectors are calculated to gauge relevance. Although useful for assessing general alignment, this approach treats thematic coherence as a basic numeric metric, failing to capture the contextual depth CARAG aims to provide. Similarly, a t-SNE visualization with Kernel Density Estimation, as shown in Figure 9(b), highlights clustering within the embedding space without indicating clear thematic boundaries. Unlike our PCA and t-SNE approach in Section 5.2, which incorporates distinct KDE representations to define thematic contours, this generic t-SNE with KDE does not offer indicators of thematic relevance, making it insufficient for evaluating CARAG's context-aware framework. In summary, these standard techniques lack the nuanced depth necessary to evaluate CARAG's thematic alignment, highlighting the need for a tailored evaluation approach.