In recent years, the machine learning-based Artificial Intelligence (AI) systems have become increasingly advanced, allowing them to interpret images and generate data sets that have previously been difficult to access. But as AI technology has become more advanced, so too has the demand for data sets that contain greater diversity in order to avoid potential biases.
To address this need, companies have begun to create synthetic images in order to add more diversity to AI data sets. These synthetic images are computer-generated, and unlike traditional AI data sets, which are limited to existing images from their sources, synthetic images can be generated without restriction. This means that a wider range of ethnicities and genders can be represented, as well as details like age, facial structure and other physical attributes.
Although using synthetic images to create more diverse data sets is laudable in terms of increasing the diversity of AI-driven solutions, there are several risks associated with their use. On the functional side, these synthetic images may not accurately reflect the real world, and this could lead to errors in the AI’s performance.
There are also moral risks that come with using synthetic images, as companies are essentially creating a “fake” population that could be used to exploit people’s trust in AI. For example, if synthetic images were used to create a dataset of drivers to be used in an autonomous vehicle, drivers in the real world may begin to question the safety of the vehicle’s performance.
In a nutshell, synthetic images can be a great way to add more diversity to AI data sets. However, it’s important to recognize the functional and moral risks that come with their use and ensure that the AI’s performance and the trust of real-world users are not compromised in any way.