# Machine Intelligence in Africa: a survey

arXiv: https://arxiv.org/abs/2402.02218 · HTML: https://mlsanity.com/paper/2402.02218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 452 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [GFlowOut: Dropout with Generative Flow Networks](https://mlsanity.com/paper/2210.12928/) (2022)
- [Towards a parallel corpus of Portuguese and the Bantu language Emakhuwa of Mozambique](https://mlsanity.com/paper/2104.05753/) (2021)
- [Household poverty classification in data-scarce environments: a machine learning approach](https://mlsanity.com/paper/1711.06813/) (2017)
- [Adapting to the Low-Resource Double-Bind: Investigating Low-Compute Methods on Low-Resource African Languages](https://mlsanity.com/paper/2303.16985/) (2023)
- [The State of Computer Vision Research in Africa](https://mlsanity.com/paper/2401.11617/) (2024)
- [Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study](https://mlsanity.com/paper/2004.13840/) (2020)
- [MphayaNER: Named Entity Recognition for Tshivenda](https://mlsanity.com/paper/2304.03952/) (2023)
- [Towards a Better Understanding of the Computer Vision Research Community in Africa](https://mlsanity.com/paper/2305.06773/) (2023)
- [Pretrained Vision Models for Predicting High-Risk Breast Cancer Stage](https://mlsanity.com/paper/2303.10730/) (2023)
- [Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models](https://mlsanity.com/paper/2306.02105/) (2023)
- [FonMTL: Towards Multitask Learning for the Fon Language](https://mlsanity.com/paper/2308.14280/) (2023)

## Built on by (most cited first, 1 of 1)

- [Machine Mirages: Defining the Undefined](https://mlsanity.com/paper/2506.13990/) (2025)

## Abstract (arXiv)

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.