Vol. 3, Issue 2, Part A (2021)

Artificial Intelligence (AI) is increasingly being used in mental health diagnostics, creating new opportunities for early identification, higher accuracy, and more access to care. Natural language processing (NLP), deep learning models, and chatbot-based evaluations are being used to analyze voice, text, facial expressions, and behavioural patterns in order to detect disorders such as depression, anxiety, and schizophrenia. Despite these advances, the use of AI in mental health presents serious ethical and equitable concerns, specifically over data bias, transparency, cultural sensitivity, and the potential of exacerbating gaps in mental health care. This review aims to explore the ethical dimensions and equity implications of AI-based tools in mental health diagnostics. The findings indicate that, while AI has diagnostic accuracy in controlled conditions, its generalizability to different populations is limited due to non-representative training datasets and a lack of cultural and contextual adaptation. Ethical issues in AI integration, including algorithmic opacity, data privacy, and the digital divide, hinder real-world integration. Strategies such as explainable AI, fairness-aware modelling, and participatory design offer potential solutions. Therefore, addressing ethical issues and structural constraints through collaborative, transparent, and context-sensitive design is critical to preventing existing gaps.