###### Abstract  
This study conducts an ethics-centered analysis of the AI/ML models used in Student Mental Health (SMH) research, considering the ethical principles of fairness, privacy, transparency, and interpretability. First, this paper 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 topic modeling techniques to depict the prevailing themes in the corpus. Finally, this study proposes novel measurable privacy, transparency (reporting and replicability), interpretability, and fairness metrics scores as a multi-dimensional integrative framework to evaluate the extent of ethics awareness and consideration in AI/ML-enabled SMH research. Findings show that (i) 65% of the surveyed papers disregard the privacy principle; (ii) 59% of the studies use black-box models resulting in low interpretability scores; and (iii) barely 18% of the papers provide demographic information about participants, indicating a limited consideration of the fairness principle. Nonetheless, the transparency principle is implemented at a satisfactory level with mean reporting and replicability scores of 80%. Overall, our results suggest a significant lack of awareness and consideration for the ethical principles of privacy, fairness, and interpretability in AI/ML-enabled SMH research. As AI/ML continues to expand in SMH, incorporating ethical considerations at every stage--from design to dissemination--is essential for producing ethically responsible and reliable research. s +
Footnote †: journal: Applied sciences  
model lacked transparency, and Apple and Goldman Sachs could not adequately explain how credit limits were calculated, or the reasons behind the observed inequitable gender discrepancies. Similarly, the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), a recidivism risk assessment tool introduced by Northpointe (now Equivant) and used by several US states, has sparked an intense debate about the ethical implications of using AI/ML models in high-stakes, judgment-intensive decisions, as studies have demonstrated significant bias against Black defendants [4]. In the mental health domain, designing reliable and objective proxies of mental health conditions represents an omnipresent struggle for researchers [5; 6; 7]. Interestingly, AI/ML models and techniques provide promising opportunities in assisting researchers with the challenging tasks of understanding and assessing mental health conditions and classifying mental disorders [8; 9; 10; 11]. Nonetheless, similar to its application in other social sciences, the use of AI/ML in the mental health field remains challenging, raising critical ethical concerns, especially due to the complexity and high-impact nature of the discipline [12]. Recently, literature reviews and research surveys on the applications of AI/ML for mental health have emerged in the medical and clinical psychology domain. This emerging literature evaluates the accuracy, reliability, and efficacy of the algorithms used in previous studies and considers prevailing opportunities and challenges for their practical implementations. Much of this work focuses on AI/ML usage in neuroscience, specifically neuroimaging research. Other new literature reviews examine algorithmic performance in predicting the outcomes of clinical interventions (e.g., pharmacological treatments) for specific mental health conditions and discuss approaches to identify key behavioral markers for clinical states from mobile mental health sensing data. For instance, ref. [10] provides an overview of the different applications of AI/ML in mental health research. This literature review reveals four major application areas, with most papers studying detection and diagnosis of mental health conditions, and others focusing on (i) prognosis, treatment, and support, (ii) public health, or (iii) research and administration.