The authors of the Dall-E 2 / unCLIP paper describe some of their efforts to mitigate biases in the paper. ML models will always exhibit the biases present in t

The authors of the Dall-E 2 / unCLIP paper describe some of their efforts to mitigate biases in the paper. ML models will always exhibit the biases present in their training dataset, without intervention. It's not really possible to remove bias from an ML model, at least not completely. Some stereotypes, but not all, are backed up by statistics. In those cases, should we completely remove the bias in the training dataset? Doing so would bias the model towards outputs that are not representative of the real world. When people say that they want to remove bias from ML models, what they really mean is that they want to manipulate the output distribution into something they deem acceptable. I'm not arguing against this practise, there are plenty of situations where the output of an ML model is very clearly biased towards specific classes/samples. I'm merely arguing that there is no such thing as an unbiased model, just as there is no such thing as an unbiased human. Unbiased models would produce no output. To get around some of these problems OpenAI restricted the training dataset (e.g. filtering sexual and violent content) and also prevent generating images with recognizable faces. This doesn't prevent bias but it does reduce the number of controversial outputs.