I'm in both 3 and 4. 4 because not being explicit about the practice is misleading at best, outsourcing the difficult task of keeping the analysis private show

I'm in both 3 and 4. 4 because not being explicit about the practice is misleading at best, outsourcing the difficult task of keeping the analysis private show how unimportant it's considered, and because big techs have a tendency to decrease privacy over time. Using clients who paid for the product as a dataset generator is also wrong. But 3 at the same time because well, it's important to evaluate the performance of the product in the field not just in the lab. There were so many cases of catastrophic failures for ML models (ex. classifying black people as gorilla) that having a tight feedback loop is important. It has to be done right, but evaluating a product that was primarily developed for (or at least by) English speakers and transfered to other domains seem like the right thing to do. All in all, I don't and wouldn't use one of those assistants because 4 outweigh 3, but it's not binary.