## 8 Discussions and Conclusions
Therefore, SMH researchers need to leverage the most effective and advanced privacy-preserving techniques, such as differential privacy, federated learning, and synthetic data generation. Differential privacy techniques add noise to the actual data to prevent the identification of individual respondents while maintaining aggregate patterns [86]. For example, an SMH study analyzing the prevalence of anxiety across the different campuses of a university could utilise differential privacy, such as the Gaussian or Laplace noise addition using IBM's Differential Privacy Library, to ensure that individual student's data cannot be traced back if the dataset is compromised. Federated learning, on the other hand, allows model training across different data sources and locations without the need for data centralization, so that data remains in its original location [87]. Let's consider an SMH study developing an ML model to predict students at risk of depression based on factors such as academic performance, class attendance, and social engagement, using datasets from several universities. These institutions could collaborate on training the common predictive ML model while keeping their students' data on site by utilizing federated learning platforms such as PySyft, and TensorFlow Federated. This approach allows the development of more accurate ML models and preserves students' privacy. As for synthetic data generation techniques, they can simulate actual data patterns without exposing participants' sensitive information [88]. Continuing with the previous example, the SMH researchers could generate synthetic datasets on the real data from one of the participating institutions using tools like CTGAN (Conditional Tabular Generative Adversarial Networks) or cloud-based platforms like Gretel.ai, and train their predictive ML model on realistic datasets without violating privacy laws or ethical guidelines. Additionally, SMH scholars must integrate and promote fairness into their research processes to ensure their findings are equitable and respectful of all students, including historically marginalized groups. Researchers are encouraged to use fairness-aware algorithms, such as reweighting or fairness constraints in model training, to reduce biases associated with demographic groups in model predictions [89]. For example, when developing an ML model to predict student's mental well-being, SMH scholars could use reweighting algorithms from the AI Fairness 360 toolkit to increase the importance of underrepresented groups such as racialized ethnicities, in the training samples. This approach ensures that minority groups contribute more substantially to the model's learning, yielding less biased and more equitable outcomes. Similarly, SMH scholars could incorporate fairness constraints in the model's training process, leveraging tools like Fairlearn, to provide assurance that the model's predictions do not unequally treat any demographic group. Furthermore, SMH researchers should adopt explainable AI (XAI) techniques, such as SHAP or LIME, to determine whether demographic features impact model outcomes, which permit them to adjust and regulate their models if certain groups are treated unfairly [90]. Also, SMH scholars should conduct systematic bias audits and fairness assessments to evaluate their model performance across demographic categories and apply appropriate corrections to ensure models are equitable for all students [91]. Moreover, open and comprehensive discussions about AI/ML ethics considerations and potential risks and concerns, that involves key stakeholders' perspectives, should be included in the AI/ML-enabled studies. Finally, multi- and interdisciplinary collaborations that involve psychology and health experts, as well as data scientists should be encouraged to enhance the clinical application of the SMH-ML models and validate their results.