I actually really think that the way Bayesian probability factors in subjective probability is key, in that even if an algorithm spits out a result, it is still

I actually really think that the way Bayesian probability factors in subjective probability is key, in that even if an algorithm spits out a result, it is still subject to human interpretation as well. I think some kind of composite decision support with both purely objective results (e.g. neural networks or other models that are purely machine based) as well as subjective beliefs could be really interesting and I still haven't seen much that does this. I think maybe reinforcement learning where human feedback becomes part of the loop is about as close as I could think of. But that is different than factoring in human input to probability calculations.