## 8 Discussions and Conclusions
For instance, this study is the result of a collaboration among an interdisciplinary team of co-authors, including two experts in data analytics, one in health, and one in psychology. Their contributions were instrumental to this SLR on the existing AI/ML-enabled SMH research, which, by design, integrates the three fields of AI/ML, health, and psychology. Our research has a few limitations. First, our corpus is primarily sourced from the engineering, information technology, and computer science fields, which could limit the scope of our findings. This narrow corpus may be attributed to the novelty of the AI/ML-enabled research in the SMH field. In addition, the complexity of AI/ML methods and techniques may discourage SMH scholars, who may have limited backgrounds in computer science, from integrating AI/ML into their studies. Therefore, we encourage future research to build on our work and expand it to other health-related and social science disciplines for greater generalizability. Second, quantification of social constructs is always very challenging, and ethical considerations and principles are not an exception. In fact, social constructs cannot be universally quantified due to several reasons. As explained by [92], they are abstract due to their intangible nature, and context dependent because they vary across different cultures and settings. For example, fairness can be defined, perceived, and measured differently in diverse situations and environments, especially in the AI context [93]. Additionally, they are multidimensional as they often encompass multiple interrelated dimensions [94]. For instance, transparency involves at least two dimensions as explained in our farmwork above, namely, reporting and replicability. As a result, social constructs quantification is susceptible to measurement bias, as it is difficult to ensure objective and consistent measures, especially in divergent contexts [95]. Thus, we invite future research to build on our innovative, yet early, work and develop more advanced metrics that could better capture the nuanced aspects of the privacy, transparency, interpretability, and fairness ethical principles. Third, our corpus excludes gray literature, which could limit the scope of our findings. We suggest that future studies expand our corpus to encompass this literature and identify any incremental insights it might provide compared to the peer-reviewed research included in our study. In the coming years, AI/ML theory and practice will continue to evolve and expand, and new ethical challenges will inevitably arise, requiring governments and regulatory bodies to adapt and innovate their legal frameworks and privacy laws to meet these emerging realities. Building on our pioneering work, future research should extend our ethics framework by incorporating and quantifying additional ethical principles critical to AI/ML-based research in SMH, including accountability, benefice, autonomy, and justice. In addition, our ethics-focused framework and analysis could be applied across various other fields, especially research areas that are increasingly relying on AI/ML methods and techniques, such as computer science, IT, health sciences, and business disciplines like finance, business analytics, and accounting. Finally, AI/ML research must dedicate more effort and resources to the development and diffusion of responsible and sustainable models and techniques that adhere to the highest ethical standards and best practices, ensuring that AI advancements benefit humanity while safeguarding against potential drawbacks or misuse [71]. Conceptualization, M.D., S.B.H., M.Z. and S.S.; methodology, M.D., S.B.H., M.Z. and S.S.; software, S.B.H. ; validation, M.D. and S.B.H. ; formal analysis, S.B.H., M.D., S.S. and M.Z. ; investigation, S.B.H., M.D. and M.Z. ; resources, S.B.H., M.D., M.Z. and S.S.; data curation, S.B.H. and M.D. ; writing--original draft preparation, M.D. and S.B.H. ; writing--review and editing, M.D., S.B.H., M.Z. and S.S.; visualization, M.D. and S.B.H. ; supervision, M.D., M.Z.