Feels like a decision tree is not the kind of algorithm that can solve this problem due to its multidimensional nature. It's very vulnerable to Simpson's paradox.
It may be right most of the time if we lump everyone in the same bag, but that is not helpful at all.
Also, having the self-evaluated unemployment risk the first deciding factor seems to defeat the purpose.
A fixed decision tree isn't machine learning. If the tree can be updated or generated by code, then sure, but a human just writing a decision tree has nothing to do with machine learning.
Re:Engage with an unemployment algorithm
by tgsovlerkhgsel on Tuesday June 14, 2022 @02:05PM
(#31743440)
The interesting thing is that at least the version on this website does NOT put non-Western immigrants into the high risk group. It puts Western immigrants (and descendants) there, and non-Western descendants, but not non-Western immigrants themselves!
It's a decision tree. Decision trees falls under ML. The thresholds are learned from data.
I would imagine they had to use a decision tree in order to tell users and case workers _why_ they are at risk.
The algorithm has been heavily criticized for putting basically all non-western immigrants into the "high risc"-group, but if the job market historically hasn't been willing to hire high performing, non-western immigrants, isn't it the whole point of building an algorithm from data? To shed light on this?