| > In other words, black defendants actually are more dangerous to release and there is no magic algorithm that bypasses this fact. You are right, but that's not the problem with the algorithm. A critical assumption with large-scale data mining is that past trends continue - the problem is that the existing data fits the algorithm. It is just a conservative What-If decision maker operates on existing facts (i.e bad present day situation), just wrapped into code (or worse, encoded as opaque literal "biases" in a decision tree). I see somewhat similar patterns in lending interest data (redline zipcode -> credit ratings) and the problem is that bigger the historical trend data, the less forgiving a "past trends" algorithm will end up being. Since this ends up being a prisoner's dilemma, if you are a rational actor in this system and the system keeps playing a defect card on you, then the obvious move is to always defect - cut your losses. Algorithms can't improve the job prospects of the people released. And that's not the algorithm's fault. And therefore, the you're right - the algorithm can't change the world beyond its output result. |
Not necessarily. It would be easy to use only recent data and ignore data from decades ago. The prediction accuracy wouldn't go down much as long as there is a large enough sample size in the recent data. It may even go up if the old data really is inapplicable now.
Then if a past trend stops happening, the old data gets purged eventually and only the post-trend data is considered.