[andosa]

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.