## 3 Materials and Methods
### Data Extraction and Classification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Title** & **Author(s)** & **Year** & **Technical Debt Type** \\ \hline
50 & Maintainability Challenges in ML: A Systematic Literature Review & [75] & [PERSON] \& [PERSON] & 2022 & Documentation \\ \hline
51 & Software Documentation is not Enough! Requirements for the Documentation of AI & [135] & [PERSON] \& [PERSON] & 2021 & Documentation \\ \hline
52 & Understanding Implementation Challenges in Machine Learning Documentation & [74] & [PERSON] \& [PERSON] & 2022 & Documentation \\ \hline
53 & “This is Just a Prototype”: How Ethics Are Ignored in Software Startup-Like Environments & [79] & [PERSON], [PERSON], \& [PERSON], [PERSON] \& [PERSON] & Ethics \\ \hline
54 & Explainable Deep Reinforcement Learning: State of the Art and Challenges & [90] & [PERSON] & 2022 & Ethics \\ \hline
55 & Managing bias in AI & [136] & [PERSON], [PERSON] \& 2019 & Ethics \\ \hline
56 & Patterns and Anti-Patterns, Principles and Pitfalls: Accountability and Transparency & [81] & [PERSON] & 2020 & Ethics \\ \hline
57 & Principles alone cannot guarantee ethical AI & [26] & [PERSON] & 2019 & Ethics \\ \hline
58 & Who pays for ethical debt in AI? & [82] & [PERSON] & 2021 & Ethics \\ \hline
59 & AI Competitions as Infrastructures Examining Power Relations on Kaggle and Grand Challenge in AI-Drive & [6] & [PERSON] & 2023 & Infrastructure \\ \hline
60 & Infrastructure for Usable Machine Learning the Stanford DAWN Project & [50] & [PERSON] et al. & 2017 & Infrastructure \\ \hline
61 & Practices and Infrastructures for Machine Learning Systems: An Interview Study in Finnish Organizations & [137] & [PERSON], [PERSON], [PERSON] \& [PERSON] & 2022 & Infrastructure \\ \hline
62 & A Meta-Summary of Challenges in Building Products with ML Components—Collecting Experiences from 4758 & [138] & [PERSON] et al. & 2023 & Model \\ \hline
63 & A Taxonomy of Software Engineering Challenges for Machine Learning Systems—An Empirical Investigation & [47] & [PERSON], [PERSON], \& 2019 & Model \\ \hline
64 & Clones in Deep Learning Code: What, where, and why? & [139] & [PERSON], [PERSON], [PERSON] \& [PERSON] & 2022 & Model \\ \hline
65 & Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software & [140] & [PERSON] \& [PERSON] & 2019 & Model \\ \hline
66 & Hidden Technical Debt in Machine Learning Systems & [5] & [PERSON], [PERSON], \& [PERSON], [PERSON], 2015 & Model \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Title** & **Author(s)** & **Year** & **Technical Debt** \\ \hline
67 & \begin{tabular}{c} Machine Learning Model Development from a \\ Software Engineering Perspective a Systematic \\ Literature Review \\ \end{tabular}  & [141] & [PERSON] et al. & 2021 & Model \\ \hline
68 & \ \begin{tabular}{c} Quality issues in Machine Learning \\ Software Systems \\ \end{tabular}  & [52] & \begin{tabular}{c} [PERSON], \\ [PERSON], \\ [PERSON] [PERSON], \\ [PERSON], [PERSON] \& \\ [PERSON] [PERSON] \\ \end{tabular}  & 2023 & Model \\ \hline
69 & \ \begin{tabular}{c} Synergy Between Machine/Deep Learning and \\ Software Engineering: How Far Are We? \\ \end{tabular}  & [142] & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON], \\ [PERSON], [PERSON], [PERSON], [PERSON] \& [PERSON] \\ \end{tabular}  & 2020 & Model \\ \hline
70 & \ \begin{tabular}{c} The ML Test Score: A Rubric for ML Production \\ Readiness and Technical Debt Reduction \\ \end{tabular}  & [7] & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], [PERSON] \\ \& [PERSON] \\ \end{tabular}  & 2017 & Model \\ \hline
71 & \ \begin{tabular}{c} Towards CRISP-ML(Q): A ML Process Model with \\ Quality Assurance Methodology \\ \end{tabular}  & [49] & \begin{tabular}{c} [PERSON], [PERSON], \\ [PERSON], \\ [PERSON], \\ [PERSON], [PERSON] \\ \& [PERSON] \\ \end{tabular}  & 2021 & Model \\ \hline
72 & \ \begin{tabular}{c} Towards Guidelines for Assessing Qualities of \\ Machine Learning Systems \\ \end{tabular}  & [143] &