Rosie Campbell

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Bias in Machine Learning
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Bias in Machine Learning

Jan 02, 2020

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Bias in Machine Learning
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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/variance trade-off

  • Bias as in weights and biases

  • Algorithmic bias:

    • 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’)

Related:

  • Notes on AI Bias — Benedict Evans

  • Even if it’s unbiased, it doesn’t mean it works


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