I guess the field is constantly making a decision between 'do we want to outperform humans, but lose interpretability' and 'we should always be interpretable'

I guess the field is constantly making a decision between 'do we want to outperform humans, but lose interpretability' and 'we should always be interpretable' Human-generated artificial data will always contain the human's assumptions. But this data might not contain the 'superhuman' element that leads to the ML-system outperforming humans, because we don't know what that is (yet). Receipts are a good example for artificial data, because they're human-made. But a lot of what we do with ML systems is using data that doesn't come from humans, images of wildlife, satellite images, biological data, etc