In 99% of use-cases with ML models like this, the output of the model won’t be the direct output of the system it’s used in. Instead, the output of the model wi

In 99% of use-cases with ML models like this, the output of the model won’t be the direct output of the system it’s used in. Instead, the output of the model will be fed through as an input to a correlational model/heuristic. I.e. Amazon doesn’t care what emotion you’re feeling. But it does potentially care whether you seem to be “in a buying mood” — e.g. it might be able to save a lot of money by constraining its ad placements to only be shown to such people. It isn’t going to try to figure out what mood “a buying mood” is — it’s just going to train another model to look at the mood-model output, together with people’s shopping histories, and then learn “people tend to buy things more often when the mood model has this output.” So it’s not like Amazon will ever assert that you’re in a particular mood. They’ll just assert that you look like someone who’s ready to buy things, with your mood being one input to that judgement. Their perception of your mood doesn’t have to be 100% accurate for that to be helpful; any more than a salesman’s read of your mood does.