The way statistical learners (algorithms) work is by using a labeled dataset of features to determine the probability a new entry should be labeled as 'these' or 'those'. You then tell it if it is correct or not. The weights of the features used in its determination are then adjusted and the new entry is added to the dataset.

The points you have control over are the labels used, the defined features and decision validation. The algorithm interprets these things by abstraction. No one has any direct control on how the algorithm correlates features and labels. We can only predict the probabilities of how the algorithm might interpret things.

In the end, the correlations drawn by the algorithm are boolean. 100% one and none of the other. All nuance is thrown out. It will determine which label applies most and that will become 'true'. If you are depressed, it will determine the most depressed you. If you are angry, it will determine the most angry you.

You can try to adjust feature and label granularity for a semblance of nuance. This only changes the time needed to determine 'true'. In the end, all nuance will still be lost and you'll be left with a single 'true'.

People already have the tools to control how their algorithms work. They just don't understand how the algorithms work so they misuse the tools they have.

Think about "Inside Out" by Pixar. You can try to make happy all the time but at some point you get happy and sad. The algorithm cannot make that distinction. It's either happy or sad.