Bias in Machine Learning
The term ‘bias’ is used in many ways in Machine Learning, and I think this leads to a lot of people talking past each other.
Examples of how the term is used:
Bias as in weights and biases
Bias in the sense of a result that doesn’t accurately reflect the real world (e.g. due to a poorly chosen training data set) possibly related to cognitive biases of programmers
Bias in the sense that the result does accurately reflect the real world but the real world is biased/unfair
Bias in the sense that the data used to train the model does accurately represent the real world, but the system learns incorrect proxies (e.g. a system that captions a woman at a computer as ‘man sitting at a computer’)