## 8 Discussions and Conclusions  
This paper conducts an ethics-centered SLR about the usage of AI/ML methods and techniques in SMH research, considering the ethical principles of fairness, privacy, transparency, and interpretability. First, this study surveys the AI/ML methods used in the extant SMH literature published between 2015 and 2024, as well as the main health outcomes, to inform future work in the SMH field. Then, it leverages advanced text-mining techniques (topic modeling, LDA) to depict the prevailing themes in the corpus. Most importantly, this paper proposes novel and measurable privacy, transparency (including both reporting and replicability scores), interpretability, and fairness metrics as a multi-dimensional integrative framework to evaluate the extent of ethics awareness and consideration in AI/ML-enabled SMH research. The main results show that only 35% of the surveyed papers include one or more of the privacy criteria. In addition, 59% of the studies exhibit a low interpretability score, using black-box AI/ML models, such as deep neural networks and random forest. Furthermore, barely 18% of the papers provide demographic information about the study's participants, such as gender, race, and age, suggesting a limited consideration of the fairness principle. Nonetheless, the transparency principle is implemented at a satisfactory level with a mean reporting score of 8 (out of 10) and an average replicability score of (4 out of 5), suggesting that SMH researchers are mindful of the importance of disclosing transparent AI/ML models for their different stakeholders. Despite the scientific advancements enabled by the usage of AI/ML methods and techniques to study SMH issues, the surveyed papers suffer from several limitations. On one hand, there is a noticeable lack of involvement of experts from Psychology and Health in the burgeoning AI/ML-based SMH research, which could partially explain the quasi absence of meaningful integration and significant incremental contribution of this research to prior work in psychology and mental health. In addition, the empirical results in the reviewed studies cannot be easily generalized due to the specificity of their contexts and their small study samples. On the other hand, several limitations exist in the application of advanced AI/ML techniques in the surveyed papers, such as the non-use of nested cross-validation and the absence of ML prediction testing on fully independent samples. Most importantly, we documented a severe lack of awareness and consideration for the ethical principles of privacy, fairness, and interpretability in AI/ML-enabled SMH research. In healthcare research, ethical priorities typically focus on (i) patient privacy, given the sensitive nature of individuals' demographic and health data, and (ii) interpretability, due to the direct and significant impact of model predictions on patient well-being [84]. In contrast, finance research emphasizes (i) fairness, to avoid systematic biases and inequitable outcomes, and (ii) accountability, because of the potential broad and substantial effects of model findings on the economy and regulatory environment [85]. Both fields are pioneers in integrating ethical considerations and implementing interpretability and bias-mitigation techniques. By comparison, SMH research is lagging in these areas and could, hence, benefit from adopting better practices from healthcare and finance research. As the use of AI and ML in SMH research continues to expand, incorporating ethical considerations throughout the research process-from design to dissemination-is essential for developing ethically responsible and highly reliable SMH research. Typically, data collected by SMH researchers contain sensitive demographic information, such as gender, age, race, and ethnicity. Participants entrust the researchers with their personal data and expect their privacy to be protected.