Deploying AI in the Global South to achieve the United Nations SDGs presents unique challenges that require careful consideration and strategic planning. Among the challenges are:

• Data Availability and Quality: High-quality, relevant, and timely data are crucial for training effective AI models. In many developing  countries, there is a  scarcity of such data due to limited data collection capabilities, privacy  concerns, and  insufficient digitization of records. Additionally, data may be biased or not representative of the diverse populations and conditions within the Global South, affecting the accuracy and fairness of AI outcomes.
• Capacity and Skills Gap: There is a gap in local expertise and skills necessary to develop, deploy, and maintain AI technologies in the Global South. The lack of local AI talent and the resources to train individuals in these technologies can impede the development of locally relevant and sustainable AI solutions.
• Ethical and Social Implications: Deploying AI in the Global South must be done with careful consideration of ethical, cultural, and social implications. Issues such as algorithmic bias, privacy, and the potential displacement of jobs are of particular concern. Without appropriate safeguards, AI systems could exacerbate inequalities or harm vulnerable populations. Ensuring that AI deployment is guided by ethical principles and includes mechanisms for accountability and transparency is crucial for gaining public trust and achieving positive social impacts.