When you watch action movies, especially American or European ones, you see scenes of hacks and IT guys accessing databases that give them crazy access to a wide array of information. AI
By Nairametrics Op-Ed Contributor
You’ll hardly see that kind of thing in Nigerian movies.
Why?
That’s because Nigeria, like many developing countries, faces the significant challenge of data limitations.
This lagging data infrastructure hinders the growth of various sectors, but none more than the advancement of Artificial Intelligence (AI) in Nigeria.
Beyond database hackers in action movies, data limitation is one of the big reasons why Nigeria is not effectively competing on the global AI stage.
We have a lot of brilliant brains, tech sissies, and IT guys building AI solutions and AI-based products on this side of the world.
However, scarcity of the much-needed data has kept AI solutions from being successfully deployed.
Dr Bosun Tijani, Nigeria’s Minister of Communications, Innovation and Digital Economy has emphasized that without data and knowledge of the people, properties, and society, there is much that cannot be done.
Why is Data so important in AI deployment?
Data is the fuel that drives AI
Without comprehensive datasets to train and refine algorithms, AI models will be unreliable and ineffective. This data scarcity puts Nigeria at a significant disadvantage.
Take a look at how South Korea’s massive investment in data collection and digital infrastructure has fueled its AI revolution.
South Korea’s government established a national AI strategy in 2020, prioritizing data sharing and collaboration between public and private sectors.
This focus on data has resulted in South Korea developing world-class AI applications in areas like healthcare, manufacturing, and transportation.
Nigeria has vast potential and lots of people with brains and drive to innovate. For that reason, we need to get more intention about developing our data to bridge the AI gap.
In Lagos, traffic congestion affects youths, families, businesses and organizations. Most people believe this problem cannot really be solved because too many areas in Lagos are landlocked. The interesting fact is, that AI can help reduce congestion by providing personalized traffic management and thus, it would save commuters precious time.
But now, the data to make such a solution and solve one of the major problems in one of Nigeria’s major cities is lacking. If decision-makers in this parlance were to create solutions that overcome data limitations, we can spark a height of innovation that addresses many of Nigeria’s most pressing challenges and propels our country towards a more prosperous future.
A Roadmap for tackling the challenge of Data scarcity
1. Synthetic Data Generation
This technique involves creating artificial data that mimics real-world data. Universities and research labs in Nigeria can collaborate to develop tools for generating synthetic datasets specific to the Nigerian context.
2. Data Augmentation
We have different databases in Nigeria with existing datasets, although they are limited. As the saying goes, use what you have to get what you want. These datasets can be amplified through data augmentation techniques. Libraries like TensorFlow offer functionalities to manipulate existing data (think flipping images, adding noise) to create more variations for training purposes.
3. Transfer Learning
Pre-trained AI models developed elsewhere can be a valuable starting point. By leveraging these models and fine-tuning them with smaller, Nigerian-specific datasets, we can adapt them to local contexts while mitigating potential biases.
4. Collaboration is Key
Data partnerships with government agencies, NGOs, and private institutions can unlock valuable datasets that may not be publicly available. Data access agreements should be clearly defined to ensure privacy compliance and responsible data use.
5. Privacy-Preserving Techniques
Differential privacy and federated learning are emerging techniques that can help collect and utilize data while protecting user privacy. These approaches add noise to data or keep training data on individual devices, enabling AI development without compromising user anonymity.
While there’s no magic bullet approach for solving data limitations, a combination of these approaches can significantly improve the data landscape in Nigeria.
Building a robust data infrastructure requires collaboration between the public and private sectors, with a focus on responsible data collection, storage, and utilization.
The future of AI in Nigeria is bright, but it hinges on our ability to address the data challenge head-on.
