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Bias (ethics/fairness): Difference between revisions
Created page with "==Introduction== Bias in machine learning refers to systematic errors or discrimination present in a model's predictions or decisions. It can arise when the data used to train the model is not representative of the population it will be applied to, or certain groups are disproportionately represented or excluded from training data. ==Sources of bias in machine learning== Biases can arise during the creation and deployment of machine learning models. 1. Data Bias: This..."
(Created page with "==Introduction== Bias in machine learning refers to systematic errors or discrimination present in a model's predictions or decisions. It can arise when the data used to train the model is not representative of the population it will be applied to, or certain groups are disproportionately represented or excluded from training data. ==Sources of bias in machine learning== Biases can arise during the creation and deployment of machine learning models. 1. Data Bias: This...") |
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