Recommendations on the Use of Synthetic Data to Train AI Models
Philippe de Wilde, Payal Arora, Fernando Buarque, Yik Chin, Thinyane, Mamello, Stinckwich, Serge, Eleonore Fournier-Tombs and Tshilidzi Marwala (2024). Recommendations on the Use of Synthetic Data to Train AI Models. United Nations University.
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Sub-type Policy brief Author Philippe de Wilde
Payal Arora
Fernando Buarque
Yik Chin
Thinyane, Mamello
Stinckwich, Serge
Eleonore Fournier-Tombs
Tshilidzi MarwalaTitle Recommendations on the Use of Synthetic Data to Train AI Models Publication Date 2024-02 Place of Publication Tokyo Publisher United Nations University Pages 9 Language eng Abstract Using synthetic or artificially generated data in training Artificial Intelligence (AI) algorithms is a burgeoning practice with significant potential to affect society directly. It can address data scarcity, privacy and bias issues but does raise concerns about data quality, security and ethical implications. While some systems use only synthetic data, most times synthetic data is used together with real-world data to train AI models. Our recommendations in this document are for any system where some synthetic data are used. The use of synthetic data has the potential to enhance existing data to allow for more efficient and inclusive practices and policies. However, we cannot assume synthetic data to be automatically better or even equivalent to data from the physical world. There are many risks to using synthetic data, including cybersecurity risks, bias propagation and increasing model error. This document sets out recommendations for the responsible use of synthetic data in AI training. Copyright Holder United Nations University Copyright Year 2024 Copyright type All rights reserved ISBN 9789280891546 -
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