I think the core issue here is that people want two things. On one hand we want our models to accurately describe reality, not an idea of what reality should be

I think the core issue here is that people want two things. On one hand we want our models to accurately describe reality, not an idea of what reality should be. On the other hand, we don't want ML to freeze society and culture in their current state, but to help decide on and drive social change. The tension between these goals arises when models trained today are used to make decisions tomorrow. One way to resolve the tension might be to add time dimension and historical training data. The models might then be able to return in addition to any prediction variable p also its time derivative dp/dt. For example, a model might then return results such as: "movies with female main character: lower sentiment, trending up; movies with male main character: higher sentiment, trending down".