## 7 Ethical Considerations in AI/ML-Enabled SMH Research
### Ethics Principles for AI/ML
#### 7.1.4 Fairness
The minimum score for the reviewed papers is 4, the mean and median are 8, and the maximum is 10. The least reported items are hyperparameter tuning procedures, followed by validation metrics, and bias mitigation and sampling strategies. Only 18 out of the 59 surveyed papers (30%) report their hyperparameter tuning procedures, whereas 29 papers (49%) disclose their validation metrics. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Principle** & **Proxies** & **Main Results** \\ \hline \multirow{3}{*}{Privacy} & • Approval of an ethics committee & \multirow{3}{*}{65\% of papers do not include any discussion about data privacy} \\  & • Consent of participants & & \\ • Data anonymization & & & \\ \hline \multirow{3}{*}{Transparency} & • Quality reporting score & \multirow{3}{*}{Relatively high scores overall} \\  & • Replicability score & & \\ \hline \multirow{3}{*}{Interpretability} & Complexity of the ML technique used (traditional ML vs. deep learning or random forest) & • & 59\% of papers use deep learning or random forest algorithms \\  & & & \\ \cline{1-1}  & & & Only two papers introduce explainable AI techniques (SHAP) \\ \hline \multirow{3}{*}{Fairness} & \multirow{3}{*}{Demographic attributes of the data sample} & \multirow{3}{*}{18\% of the papers provide information about the composition of its sample in terms of race, gender, or age} \\ \cline{1-1}  & & & \\ \cline{1-1} \cline{4-4} \cline{5-5}  & & & \\ \cline{1-1} \cline{4-4} \cline{5-5}  & & & \\ \hline \end{tabular}
\end{table} 
Table 2: An assessment model for awareness and implementation of AI/ML ethics principles. We, then, propose a third score, replicability, which is also derived from the standards of [78]. In this article, the authors stipulate that, in order to publish replicable AI/ML-enabled research, the following information must be disclosed in the paper: the size of the data sample, the number of variables, the algorithm(s) employed, at least one validation metric, and explicit performance metrics. Inspired by the authors' recommendations, we develop a replicability score that ranges from 0 to 5 accounting for each of these information items. This score is computed using the same coding procedure as the reporting score. Figure 16 depicts our analyses of the surveyed papers indicating that the average replicability score is 4, the minimum is 2, and the least reported item is the validation metric. In addition, 49% of the studies have a high replicability score  
Figure 14: Number of papers by privacy score. Figure 15: Number of papers by reporting quality score. \begin{table}
\begin{tabular}{l l} \hline \hline
**Proposed Standards for Study Design and Methods Reporting** \\ \hline Data source (API, scraping, etc.) & Explicit number of features/variables \\ Bias mitigation and sampling strategies & Variable/feature reduction techniques \\ Number of data points/samples & Hyperparameter tuning procedures \\ Source of all features/variables & Validation metrics \\ Error analysis and explanation & Explicit performance evaluation measures \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standards for reporting methods and study design (extracted from [78]). In AI/ML-enabled SMH research, certain transparency metrics like hyperparameter tuning and post-hoc analysis techniques are often neglected because the primary focus is typically high-level insights into SMH patterns and risk factors rather than the technical details and performance fine-tuning of the ML models. In addition, authors may lack knowledge about rapidly evolving AI/ML tools and techniques [79]. However, omitting transparency metrics can lead to significant variability in model performance, interpretability, and convergence, ultimately compromising research replicability. In other words, future studies may not be able to reproduce the results of the original study and obtain significantly inconsistent findings, which could undermine the validity and generalizability of the initial research [80].