The tl;dr is multicollinearity is a big problem for interpreting models like this. Aka you have variables in the model that are highly correlated and because of

The tl;dr is multicollinearity is a big problem for interpreting models like this. Aka you have variables in the model that are highly correlated and because of this the model does not necessarily assign weights to the variables in a way that means anything. Eg: In the article they show "Age: 34 or Older" = -.11 and also that "Married" = -.49. Being over 34 and married is -.60. But as far as we know the model doesn't account for the fact that people older than 34 are more likely to be married in the first place. Perhaps the model would have worked just as well if "Age: 34 or Older" = -.30 and also that "Married" = -.30 (still -.60). And so on with every possible combination of attributes (they are all correlated). This isn't the end of the story. In situations where interpretability really matters or you have the time/resources, it's possible to mostly prevent this by building the model slowly and diagnosing multicollinearity issues as they arise. Perhaps you purposefully leave out variables because you see they are already highly correlated with existing ones and leave the model weights unstable. It requires exhaustive analysis and necessarily involves subjectivity, though. The researchers here could have done something like this, it's certainly done in industry for the aforementioned financial regulatory environments. Non-linear models like trees don't have multicollinearity problems because they implicitly model interactions between variables with the branching behavior. Instead the problem is interpretation, it's hard to sum up all those branches in a way that is intuitive and meaningful.