## 7 Ethical Considerations in AI/ML-Enabled SMH Research
### Ethics Principles for AI/ML
#### 7.1.4 Fairness
For example, if hyperparameters-such as learning rate and regularization terms-are not disclosed in the paper, researchers attempting replication will probably select different hyperparameters resulting in a model that performs either significantly better or worse than the original, and that yields dissimilar or even misleading results. Such inconsistencies can impede the reliable advancement of SMH research [80]. The interpretability principle revolves around the ability and easiness of interpreting AI/ML models' components and internal functioning. Therefore, we propose to assess interpretability based on the simplicity (vs. complexity) of the AI/ML techniques used in the paper. Ref. [73] argues that the AI/ML model is considered highly interpretable if it utilizes traditional ML methods, such as SVM, Naive Bayes, and logistic regression. Conversely, the model is assessed low on the interpretability spectrum if it involves more complex ML methods, such as deep learning and random forest. Accordingly, our interpretability score ranges between 0 to 2, taking the value of 0 if the paper uses complex models only, 1 if it combines complex and simple models, and 2 if it utilizes simple models only. As depicted by Figure 17, 59% of the reviewed papers use at least a deep learning or a random forest algorithm, and, therefore, rank low on interpretability. The remaining papers (41%) rank high in interpretability because they use only simple and easily interpretable algorithms. Whether in SMH research or in real-world applications, the choice between simple AI/ML models, such as decision trees, and complex models, like deep neural networks, often hingers on the trade-off between interpretability and accuracy. Simple models are preferred when understanding how model predictions are made is essential [81]. This is particularly valuable in fields like healthcare, where clear justification of model outcomes is required. However, simpler models often lack the accuracy and predictive power of complex models and struggle to capture intricate relationships, leading to less valid and generalizable predictions [71]. Complex models are especially valued in fields that require nuanced understanding of high-dimensional or unstructured data, particularly in today's increasingly complex and data-rich environment. Therefore, similar to real-world applications, we encourage SMH researchers to adopt a balanced, hybrid approach, which combines the predictive strength of complex models with the transparency of simpler ones. In addition, we advocate for a greater integration of advanced interpretability techniques in deep learning models such as SHAP or LIME. Figure 16: Number of papers by replicability score. Finally, the fairness principle and its ensuing ethical concerns are mainly due to AI/ML models' intensive data-driven nature. Therefore, we assess awareness about this principle based on the demographic attributes of the data used by the model including gender, ethnicity, age, religion, and other socio-economic group features. For simplicity, a binary metric is used. It takes the value of 0 if the paper does not disclose any demographic information about the sample and the value of 1 otherwise. Interestingly, only 11 of the surveyed studies (18%), with only 1 paper for 2015-2020 but 10 papers in the 2021-2024 period, provide demographic details about the composition of its sample, such as race, gender, and age (Figure 18). These findings illustrate a relative lack of demographic considerations in AI/ML-enabled SMH research, likely due to significant obstacles in collecting and reporting demographic data [71]. First, demographic data such as gender, race, and ethnicity are sensitive due to their private nature, especially in mental health contexts. For instance, some students may hesitate to share this information because of potential stigma or discrimination concerns, leading to incomplete data.