## 7 Ethical Considerations in AI/ML-Enabled SMH Research
### Ethics Principles for AI/ML
#### 7.1.4 Fairness  
The fairness principle concerns how AI/ML models may replicate and amplify biases rooted in societal inequity and discrimination, largely due to their intensive data-driven nature. For example, if the data used for the AI/ML model's training, testing, or validation reflects social disparities, such as historical biases, or if the data collection and preparation methods result in the under-representation of some demographic or racialized groups, the model's predictions can be biased and unfair toward already marginalized groups in society [73]. An Actionable Framework for the Assessment and Implementation of Ethics Principles in AI/ML-Enabled Research  
In this section, we analyze manually, our 59 surveyed papers from an ethics-centered perspective and evaluate the extent to which each paper describes and/or addresses the four AI-ML ethics principles discussed above in their research design/protocol. All manual analyses of the reviewed papers, described in this section, were conducted independently by two researchers and a research assistant to ensure accuracy, and minimize bias and errors. Any discrepancies that arose during the process were thoroughly discussed and resolved collaboratively to maintain consistency and reliability in the findings. Echoing a recent report by [15], a significant proportion of the papers reviewed in this report (72%) do not include any mention of ethics even though they investigate a sensitive area of healthcare research. Of the remaining papers, the vast majority (80% of them) report the implementation of some ethical review process (such as institutional REB approvals or exemptions), a procedure that is mainly related to the privacy principle. In order to systematize and reinforce our study's assessment of the implementation of ethics principles in AI/ML-based SMH research, we introduce a novel evaluation model,which involves several new AI/ML ethics metrics. As explained in Table 2, the proposed assessment model includes five AI/ML ethics scores and proceeds as follows. First, the privacy principle is proxied by the (i) approval of ethics committees or boards, (ii) obtaining the consent of participants, and (iii) the existence of procedures for data anonymization. To measure the extent of implementation of the privacy principle in each paper, we use a score that takes a value between 0 and 3, with 0 referring to the absence of the three elements related to the privacy principle (approval of an ethics committee, consent of participants, data anonymization), and the 1-3 values indicating the presence of the corresponding number of privacy elements. As shown in Figure 14, only 21 out of the 59 reviewed papers (35%) include at least one of the privacy elements, despite the widespread use of sensitive data (mental health questionnaires, mobile and sensor data, etc.) in our corpus. The absence of an appropriate awareness and consideration of the ethical issues related to privacy and data protection in most of our corpus (65% of the papers) is very alarming. Therefore, more work within the SMH research community is needed to raise the researchers' consciousness about the importance of these basic, yet critical, ethical concerns when applying AI/ML methods. Of note, this lack of awareness could be partially explained by the quasi-absence of collaborations with experts from the psychology and health spheres in the surveyed studies as outlined in Section 3 above. The second principle, transparency, is all about providing detailed descriptions and explanations about the AI/ML model's architecture, data, parameters, and optimization approaches and functions. Accordingly, the implementation of this principle is proxied by two scores: reporting or disclosure quality, and replicability. Inspired by the standards for reporting methods and study design proposed by [41], we develop a reporting quality score for each paper. This score includes ten elements, as indicated in Table 3, and ranges between 0 and 10. Figure 15 shows the number of papers by reporting scores.