The Use of Synthetic Data to Train AI Models: Opportunities and Risks
  for Sustainable Development
Tshilidzi Marwala Eleonore Fournier-Tombs Serge Stinckwich
In the current data driven era, synthetic data, artificially generated data
that resembles the characteristics of real world data without containing actual
personal information, is gaining prominence. This is due to its potential to
safeguard privacy, increase the availability of data for research, and reduce
bias in machine learning models. This paper investigates the policies governing
the creation, utilization, and dissemination of synthetic data. Synthetic data
can be a powerful instrument for protecting the privacy of individuals, but it
also presents challenges, such as ensuring its quality and authenticity. A well
crafted synthetic data policy must strike a balance between privacy concerns
and the utility of data, ensuring that it can be utilized effectively without
compromising ethical or legal standards. Organizations and institutions must
develop standardized guidelines and best practices in order to capitalize on
the benefits of synthetic data while addressing its inherent challenges.