Wait, what in the world are you talking about?! The paper is about improving face attribute detection by using demographic information. They improve performanc

Wait, what in the world are you talking about?! The paper is about improving face attribute detection by using demographic information. They improve performance on some standard benchmarks by doing this. The only way your characterization possibly makes any sense is if detecting facial characteristics is actually objectively more difficult for certain genders/races, which: 1. AFAICT isn't the case for most people (actually, I don't know anyone for whom this is the case, but I suppose it's possible such people exist). 2. Even if this were the case for humans, which it's not, why would we want algorithms to also be artificially handicapped at smile detection? That would be like building a calculator that messes up multiplication every once in a blue moon and takes a long time to do certain division operations and is worse at division than multiplication. Makes no god damn sense. Why the fuck would you want that? We know the right answer to "is this person smiling?". Why would we want a computer program that is bad at answering that question for particular subgroups? > Oops the data must be wrong The data isn't wrong. The baseline model's prediction is wrong (about simple shit like "is this person smiling?"). Using demographic representations while withholding demographic inference from the downstream face attribute detection improves the model's performance. At being correct. About simple shit like detecting smiles. Seriously... what exactly are you claiming is wrong with the paper's methodology/setup/motivation? Did we read the same paper? Do you have trouble detecting smiles on women/men? And if so, do you think computers should have the same difficulty?