1、INDEPENDEN T P U B L I C AT I O N BYRACONTEUR.NETmodel the data-generation process.Unfor-tunately,this also means losing a lot of the nuances and intricacies in the real data.”This is easy to identify from a curso-ry glance at some of the faces that have been synthetically generated theyre unlikely

2、to fool a person into thinking theyre real.Datagen is currently invest-ing in its photorealism capabilities,but Chakon argues that realism isnt crucial for every application.“If you are developing a blemish detec-tion AI for makeup application,having the detail is important,”he says.“But if youre de

3、veloping a security system,its much less relevant whether you can identify small details on a persons face.”Synthetic data also isnt a silver bullet for AI bias;it relies on the people generat-ing the data to use such platforms respon-sibly.Rei adds:“Any biases that are present in the data-generatio

4、n process whether intentionally or unintentionally will be picked up by models trained on it.”An Arizona State University study showed that when trained on predomi-nantly white,male images of engineering professors,its generative model ampli-fied the biases in the dataset,mean-ing that it produced i

5、mages of minority modes less frequently.Even worse,the AI began“lightening the skin colour of non-white faces and transforming female facial features to be masculine”when generating new faces.With synthetic data programmes giving developers access to unlimited amounts of data,this has the potential

6、to drastical-ly exacerbate the issue of bias if errors are made at any point in the generation process.If used correctly,synthetic data may still help to improve the diversity of some datasets.“If the data distribution is very unnatural for example,it doesnt contain any examples of people from a par