AN OPTIMIZATION-BASED APPROACH TO BALANCING ETHICAL AND LEGAL OUTCOMES IN ARTIFICIAL INTELLIGENCE APPLICATIONS FOR MENTAL HEALTHCARE

Abstract

Through the prompt advancement of artificial intelligence (AI) in mental healthcare, there becomes a better and more effective diagnosis, personalized treatment, and a much easier patient management. However, the use of AI encounters certain difficulties as ethical issues like biased algorithms, patient data safety, and the absence of human compassion and legal issues including a data protection regulation and liability. Therefore, this academic work is the report of the study that was principally executed by a structured questionnaire. The questionnaire will be taken by 150 participants, who are each divided into groups of AI developers, mental health practitioners, and legal practitioners. The data received will then be subject to the descriptive and correlational statistical analysis. The results disclosed that the interviewees highly valued the issue of ethics and at the same time credited AI with its efficiency, but they were still quite skeptical about AI systems and this was chiefly due to the lack of transparency and accountability. The project, therefore, advocates for ethicality, legality, and trust to be in equilibrium by clearly focusing on the trust factor. Besides, the analysis suggests that the use of optimization-based frameworks and responsible, transparent, and compliant AI applications are both ethical and legal in mental healthcare. The researchers of this study provide guidance for policy-makers, clinicians, and developers on the measures to be taken to gain trust, protect patient rights, and maximize the efficiency of AI.