Perhaps someone who knows the topic can say whether this mathematical phenomena of "adversarial examples" for neural networks can be translated to random forests: http://www.kdnuggets.com/2015/07/deep-learning-adversarial-e...
Barely perceptible changes to an image cause it to be misclassified by neural networks which never saw the image before (this is important, because it rules out simple overfitting). As a non-expert, this suggests to me that, at least for some algorithms, the reasoning "statistical algorithm estimates 99.999% probability of guilt" implies "guilty beyond a reasonable doubt" is unreliable at best.
There is great potential here for safe, effective government if machine learning output is only ever input for humans with common sense, life experience and the ability to interact with the world.