Many people believe that machine learning algorithms are analytical -- that they ponder over the data available and do logical, unbiased reasoning using some internal model.
They're the opposite of that: they're intuitive. They do pattern recognition. They're System 1, not 2.
They're the opposite of that: they're intuitive. They do pattern recognition. They're System 1, not 2.
Machine learning models take the shortest path from input to label, based on previous situations they've encountered -- just like human intuition. Straight input-to-output mapping.
And of course, they tend to be highly biased.
And of course, they tend to be highly biased.
Replies indicate that people are very confused about what "bias" means. It means doing pattern recognition based on spurious correlations, as opposed to causal reasoning. A ML model will use all correlations found in the training data, and typically many of them will be spurious.
