# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## Appendix A
\begin{table}
\begin{tabular}{l l l} \hline
**Dimension** & **Value** & **Papers** \\ \hline Type of Drift & \multicolumn{1}{c}{Gradient Concept Drift} & [41, 64, 146] \\ \cline{2-3}  & \multicolumn{1}{c}{Incremental Concept Drift} & [35, 51, 64] \\ \cline{2-3}  & \multicolumn{1}{c}{Recurrent Concept Drift} & [51] \\ \hline Application Context & \multicolumn{1}{c}{Financial} & [146] \\ \cline{2-3}  & \multicolumn{1}{c}{Image Classification} & [35] \\ \cline{2-3}  & \multicolumn{1}{c}{IoT} & [41] \\ \cline{2-3}  & \multicolumn{1}{c}{Network Traffic} & [51] \\ \cline{2-3}  & \multicolumn{1}{c}{Healthcare} & [64] \\ \hline Scope & \multicolumn{1}{c}{Global} & [35, 41, 51, 64, 146] \\ \hline Audience & \multicolumn{1}{c}{End-Users} & [146], [41] \\ \cline{2-3}  & \multicolumn{1}{c}{Data Scientists} & [35, 41, 51, 64] \\ \hline Metrics & \multicolumn{1}{c}{IoU} & [35] \\ \hline Learning Mode & \multicolumn{1}{c}{Hybrid} & [64, 146] \\ \cline{2-3}  & \multicolumn{1}{c}{Offline} & [35] \\ \cline{2-3}  & \multicolumn{1}{c}{Online} & [41, 51] \\ \hline \end{tabular}
\end{table} 
Table 16: Classification of Prototype/Medoids and Exemplar Tracking literature based on key dimensions. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Value** & **Papers** \\ \hline Type of Drift & \begin{tabular}{l} Conceptual Drift (General) \\ \end{tabular} & [145] \\ \cline{2-3}  & \begin{tabular}{l} Data Drift \\ \end{tabular} & [42,145] \\ \hline Application Context & \begin{tabular}{l} Financial \\ \end{tabular} & [42,145] \\ \hline \multirow{2}{*}{\begin{tabular}{l} Scope \\ \end{tabular} } & \begin{tabular}{l} Global \\ \end{tabular} & [42,145] \\ \cline{2-3}  & \begin{tabular}{l} Local \\ \end{tabular} & [42,145] \\ \hline Audience & \begin{tabular}{l} End-Users \\ \end{tabular} & [42,145] \\ \hline \multirow{2}{*}{\begin{tabular}{l} Application Context \\ \end{tabular} } & \begin{tabular}{l} Financial \\ \end{tabular} & [42,145] \\ \hline \multirow{2}{*}{\begin{tabular}{l} Audience \\ \end{tabular} } & \begin{tabular}{l} Local \\ \end{tabular} & [42,145] \\ \hline \multirow{2}{*}{\begin{tabular}{l} Audience \\ \end{tabular} } & \begin{tabular}{l} End-Users \\ \end{tabular} & [42,145] \\ \hline \multirow{2}{*}{\
\begin{tabular}{l} Metrics \\ \end{tabular} } &
\begin{tabular}{l} — \\ \end{tabular} & [42,145] \\ \hline \hline \end{tabular}
\end{table} 
Table 31: Classification of Human-in-the-Loop and Pedagogical XAI literature based on key dimensions. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Value** & **Papers** \\ \hline Type of Drift & \begin{tabular}{l} Conceptual Drift (General) \\ \end{tabular} & [29,57,115] \\ \cline{2-3}  & \begin{tabular}{l} Data Drift \\ \end{tabular} & [44] \\ \hline Application Context & \begin{tabular}{l} Business Process Management \\ \end{tabular} & [115] \\ \cline{2-3}  & \begin{tabular}{l} Healthcare \\ \end{tabular} & [44,57] \\ \cline{2-3}  & \begin{tabular}{l} Software Engineering \\ \end{tabular} & [29] \\ \hline Scope & \begin{tabular}{l} Global \\ \end{tabular} & [29,44,57,115] \\ \hline Audience & \begin{tabular}{l} Data Scientists \\ \end{tabular} & [29,44,57,115] \\ \cline{2-3}  & \
\begin{tabular}{l} End-Users \\ \end{tabular} & [29,57] \\ \hline Metrics & - & - \\ \hline Learning Mode &
\begin{tabular}{l} Offline \\ \end{tabular} & [29,44,57,115] \\ \hline \hline \end{tabular}
\end{table} 
Table 29: Classification of Performance Baseline and Metric Audit literature based on key dimensions.