Artificial intelligence (AI) is transforming companies and economies worldwide, including in Africa. Data is an essential component in the training of AI systems. Unfortunately, the lack of accurate, high-quality data is a significant impediment in Africa. Synthetic data, or data generated by a computer, may be used to train AI models. While synthetic data offers tremendous opportunities for African innovation and development, it poses significant risks that must be carefully assessed.
Gathering real-world data in many African regions is difficult, expensive, and time-consuming.
These obstacles are brought about by various factors, including social, cultural, technological, and infrastructural deficiencies. Understanding and managing varied cultural traditions and languages may be challenging, especially when gathering data from multiple locations and people. As a result, synthetic data provides a solution by enabling researchers and businesses to create massive databases tailored to their specific needs.
Data poverty is a lack of access to high-quality data and the ability to use such data appropriately. It’s a significant issue that hinders economic progress, decision-making, and innovation and worsens poverty. The data availability and consumption discrepancy between Africa and the rest of the world reflects more profound disparities and has significant repercussions.
As a result, critical innovations in health, education, and transportation operate better for the rest of the globe than for Africa. For example, due to Africa’s relative data scarcity, AI systems in Africa have fewer data sets to learn from than the rest of the globe.
Synthetic data is presented as a strategy to supplement this data scarcity. The generative adversarial networks (GANs) AI method is often used to create synthetic data. GANs consist of two neural networks (akin to an artificial brain), the generator and the discriminator. The generator produces new data, while the discriminator discriminates between real and fake data.
After they are trained adversarially (i.e. generator producing synthetic data while the discriminator differentiating fake from real data), the generator and discriminator become as good as possible at their respective tasks. One benefit of using synthetic data is that personal or sensitive information is frequently eliminated, minimizing the risk of privacy violations. This is particularly important in businesses where data privacy is vital, such as healthcare. Furthermore, by making data more available, synthetic data may lower the barriers to entry for firms and inventors.
However, synthetic data has significant drawbacks. One of them is that the models used to generate synthetic data significantly influence its quality. It reflects limitations in data (such as bias and small size), resulting in compromised and less accurate AI models and potentially perpetuating inequality, injustice and poverty. This is not the intended consequence, so there is a need to develop ethical guidelines for using synthetic data. These should first guarantee that synthetic data is anonymized for privacy protection and does not include identifiable information that may be connected back to persons.
Second, synthetic data must adhere to data protection norms and regulations governing personal information use. Third, synthetic data should accurately replicate the underlying real-world data without inserting biases or distortions. One way to achieve this is to increase data size used to generate synthetic data. Fourth, synthetic data and the processes used to create it should be disclosed.
Fifth, explicit procedures for generating, managing, and using synthetic data should be developed and publicized. Sixth, detect and prevent misuse, as well as monitor and audit the use of synthetic data regularly. Seventh, acquire informed consent from individuals whose data were utilized to produce synthetic data, if applicable. Eighth, clarify how synthetic data will be utilized and what privacy controls will be in place. Ninth, prevent biases in synthetic data from reinforcing stereotypes or discrimination.
Tenth, keep cultural norms and values in mind while developing and exploiting synthetic data, especially when it represents different populations. Eleventh, since GANs are used to generate synthetic data and are computationally costly, consider the environmental effect of data collection, storage, and production and seek to offset any negative impacts. Twelfth, establish specific processes for exchanging synthetic data with other institutions or researchers while meeting ethical standards.
Thirteenth, collaborate with relevant stakeholders, such as ethics committees or review boards, to ensure that synthetic data activities are handled ethically. Synthetic data has the potential to be a game-changer in Africa’s AI journey, managing data scarcity, increasing privacy, and encouraging innovation. As Africa embraces AI and synthetic data, a balanced approach is essential. Policymakers, companies, and academics must work together to guarantee that synthetic data is used correctly and ethically.