## 4 Discussion
To ensure equity, openness, and the reduction of prejudice in such systems, regular measures must be implemented. Taking bias and misclassification into account, the two datasets were not balanced at the time of their creation. Cross-validation methods with 5 \(\times\) 5 folds were employed to eliminate randomness and lower the risk of misclassification in order to mitigate bias and fairness [49; 50];
* **Ethical Implementation and Monitoring:** When implementing such models in public settings such as schools, prisons, or mental hospitals, it is crucial to include expert teams in mental health and interdisciplinary fields. The morality of these models must be guaranteed in order to avoid putting people who are already in danger at needless risk [49; 50];
* **Accountability and Human Agency:** Machine learning models are meant to support human decision-making. Experts who can comprehend the model's behavior and predictions in a larger context should be the ones to use it on a case-by-case basis. To guarantee such functionality when working with vulnerable groups, formal accountability structures ought to be put in place;
* **Patient Consent and Privacy:** The data utilized for this research came from free, open-source datasets with audio and visual files suitable for academic purposes. Because of the nature of the dataset, there was no need for a consent form. This study did not require or involve the utilization of an institutional review board (IRB) or ethics board. Both the video and audio information were anonymized right from the source, thus no harm or risk was caused to the targeted application. The dataset was public and did not have any personally identifiable information (PII) or direct interaction with the patients. Both the existing and new models utilized 5 \(\times\) 5 fold cross-validation in order to eliminate random prediction and unethical data bias. In real-world clinical applications, patient privacy and consent are of major importance, and any system developed on top of the model outlined here would have to be completely compliant with legal and ethical standards for data protection. Specifically, the following would have to be in place to preserve patient privacy and regulatory compliance, such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA):  
1. **Data Encryption:** Any sensitive patient data (audio, video, and behavioral logs) in real clinical settings needs to be encrypted both in transit and in storage. This would ensure that unauthorized access to data is prevented, upholding patient confidentiality in storage as well as in communication between systems;
2. **Access Permissions:** There must be strict access control mechanisms such that sensitive information is viewable or analyzable only by authorized personnel. Role-based access control (RBAC) must be followed such that health care providers, researchers, and clinicians view information related to their roles only. Auditing trails must also be conducted to track access to sensitive information and assign accountability;
3. **Data Minimization:** According to the GDPR and HIPAA regulations, patient data should be maintained to the minimum required for clinical or research use. This can be achieved by adopting approaches such as anonymization or pseudonymization in a way that non-personally identifiable information is used unless explicit consent is provided;
4. **Regulatory Compliance:** Any data collected and processed in real clinical practice has to be compliant with relevant privacy legislation, i.e., GDPR for European patients and HIPAA for United States patients. This includes providing the patient with sufficient and comprehensible information on how their data will be processed, informed consent prior to collection of the information, and providing patients with the option to revoke consent at any time.