Random forests being black boxes is something that is widely touted even among active practitioner, but not really true any more. There are methods available for decomposing random forest predictions into feature contributions, so that each prediction is represented as the sum of the bias term and contribution from each feature (similar to what you get in linear regression, but at the level of each individual prediction instead of one for the whole model), see for example http://blog.datadive.net/interpreting-random-forests/. Also there are methods for extracting, pruning and summarizing random forest rules to make them human readable, see for example inTrees package in R.

This is pretty much exactly the message of a talk I attended at CU Boulder. The Microsoft researcher spoke of how linear regression for the classification of whether a given medical patient will be re-admitted to the hospital offered much more valuable insight into the relationships different diseases had to the probability of re-admittance.

Moreover, not-so-obviously strange rules learned by more complex models were exposed as strange by the information-dense pictorial representation of linear regression.