Speaker
Description
Depression remains one of the most pressing mental health challenges globally, with a significant impact in low- and middle-income countries like Kenya. Limited access to trained mental health professionals, stigma, and underfunded systems hinder early detection and treatment. This paper explores how Artificial Intelligence (AI) can be leveraged to address these gaps in community mental health settings across Kenya. Through technologies such as Natural Language Processing (NLP) and machine learning, AI can analyze social media posts, voice patterns, and mobile usage data to identify early signs of depression with notable accuracy. Integrating AI-driven tools such as mobile chatbots like a hypothetical Swahili-based assistant "Ubongo" within widely used platforms like WhatsApp offers a scalable and culturally relevant approach to screening. Further, embedding these tools into Kenya’s existing Community Health Promoter (CHP) network enhances reach and impact, enabling real-time mental health triage during household visits. However, successful implementation requires addressing several ethical and practical concerns. Issues of data privacy, algorithmic bias, digital exclusion, cultural sensitivity, and public trust are paramount. Locally relevant datasets and inclusive design are essential to ensure that AI tools resonate with Kenyan users and do not perpetuate harm. Ultimately, while AI presents a transformative opportunity for mental health care, its application must be ethically grounded, contextually adapted, and community-driven to improve early intervention and reduce the burden of depression in Kenya and beyond.
