How does bias manifest differently in machine learning systems compared to traditional programming, and what approaches can address this?

ML bias is systematic and requires diverse training data

ML bias is less significant than traditional programming bias

ML bias is random and can be fixed with more data

ML bias only affects accuracy and can be ignored