Artificial General Intelligence (AGI) for Ghanaian Development

Abstract

The pursuit of Artificial General Intelligence (AGI) must be contextualized to address the world's most pressing problems. This Masters in Computer Science research proposal outlines a framework for developing AGI systems specifically tailored to Ghana's unique developmental challenges, focusing on agriculture and public health. We argue against the monolithic, data-hungry models emerging from the Global North and instead propose a path toward a more robust, data-efficient, and causally-aware AGI. This work will focus on creating systems that can reason under uncertainty with sparse, multilingual, and multimodal data—the reality of many domains in Ghana.

Key Research Questions for AGI in Ghana

1. Data-Efficient Learning: How can we build versatile AI models that learn effectively from small, fragmented, and noisy datasets, such as agricultural data from smallholder farms in Ghana or clinical notes in multiple local languages? This requires a move beyond big data paradigms to focus on transfer learning, few-shot learning, and meta-learning.
2. Causal Reasoning: To be truly useful, an AGI must not only find correlations but understand cause and effect. How can we build a system that can reason causally—for instance, to determine not just that a crop is failing, but *why* it is failing (e.g., soil deficiency vs. pest vs. water stress) and recommend the most effective intervention?
3. Robustness and Interpretability: How can we ensure that AI systems are robust and trustworthy when deployed in complex, real-world Ghanaian environments? This involves developing interpretable models whose decision-making processes can be understood and verified by local experts, from agronomists to healthcare workers.
4. Value Alignment and Co-Design: How do we ensure that AGI systems are aligned with the socio-cultural values of Ghanaian communities? This research will employ participatory co-design methodologies, working directly with end-users in Ghana to define problems and validate solutions, avoiding the pitfalls of "techno-solutionism."

Proposed Masters in Computer Science Research: A Causal AGI Agent for Cocoa Farming in Ghana

This thesis will focus on the design, implementation, and evaluation of a modular, causal AGI agent to support Ghanaian cocoa farmers. The system will integrate three core components:

• A Perception Module: A vision transformer (ViT) pre-trained on diverse agricultural datasets and fine-tuned on a locally-curated dataset of cocoa plant imagery (satellite, drone, and ground-level). This module will be optimized for few-shot identification of diseases (e.g., Cacao Swollen Shoot Virus), pests, and nutrient deficiencies.
• A Causal Inference Engine: A structural causal model (SCM) built as a directed acyclic graph (DAG), incorporating expert knowledge from Ghanaian agronomists and soil scientists. This module will take inputs from the perception module and other data sources (weather, soil type) to infer the probable causes of crop stress.
• A Language Module: A lightweight, distilled large language model (LLM) fine-tuned to communicate actionable advice to farmers in both simple English and Twi via a basic SMS or voice interface. The model will be trained to provide explanations for its recommendations, enhancing trust and user understanding.

Methodology & Evaluation

The research will employ a multi-stage methodology. Initially, the components will be developed and tested in simulation. The core of the thesis will be a field study conducted in partnership with the Cocoa Research Institute of Ghana (CRIG). The system's performance will be evaluated against a human expert baseline on metrics of diagnostic accuracy, intervention effectiveness, and data efficiency. Crucially, a qualitative evaluation will be conducted with a cohort of Ghanaian farmers to assess the system's usability, trustworthiness, and practical value.

Broader Impact for Ghana and Africa

This research aims to establish a new paradigm for AGI development—one that is problem-driven, context-aware, and built on principles of data sovereignty and local empowerment. By open-sourcing the models and methodologies, this work will provide a blueprint for creating similar AGI systems for other critical sectors in Ghana and across Africa, from public health surveillance to natural resource management. It is a step toward a future where the most advanced technologies are designed by and for the communities they are meant to serve.