An algorithm will aim to make you look similar to what other people like you look like. It will not push boundaries or riff on creative 'happy accidents' because it can't.
An algorithm can aim to push boundaries and riff on creative 'happy accidents' in ways that people like you like. It's not even that explicit with typical supervised learning/recommenders. It will find things people like you like, whether that means pushing boundaries in ways those people like or just making you look like they like.
I feel the same as you do about ML being in too many places too confidently, but you're assuming a lot about the kinds of algorithms used.
There is no way for you to buck the pattern here since it is using a statistical estimation for whether you like something. You could very well be an outlier datapoint rather than someone in the median, at which point the model fit for you is extremely poor. If you had an actual person on the other end you could tell them explicitly what you want rather than have some likelihood estimation based on metadata that might not have any relevance to this particular decision you are making at this time.
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An algorithm can aim to push boundaries and riff on creative 'happy accidents' in ways that people like you like. It's not even that explicit with typical supervised learning/recommenders. It will find things people like you like, whether that means pushing boundaries in ways those people like or just making you look like they like.
I feel the same as you do about ML being in too many places too confidently, but you're assuming a lot about the kinds of algorithms used.
There is no way for you to buck the pattern here since it is using a statistical estimation for whether you like something. You could very well be an outlier datapoint rather than someone in the median, at which point the model fit for you is extremely poor. If you had an actual person on the other end you could tell them explicitly what you want rather than have some likelihood estimation based on metadata that might not have any relevance to this particular decision you are making at this time.