Balanced how? It could be balanced to represent a population, but which population? The US? The university where it was created? The world? It could also be ba

Balanced how? It could be balanced to represent a population, but which population? The US? The university where it was created? The world? It could also be balanced so that the evaluation metrics were similar for each subgroup (possibly ending up with a sample that's very different from the population). But what are the subgroups? For any commonly used definition of race, there is a lot of intra-group variety. Maybe balancing the training data is enough. But figuring out what balance even means is a huge question. I don't think there's a ready CS/stats solution to this problem, so it will require interdisciplinary engagement and listening to the people who have been on the wrong end of facial-recognition bias is likely a place to start.