Definition Of Bias In Machine Learning
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Machine learning bias also sometimes called algorithm bias or ai bias is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.
Definition of bias in machine learning. Bias in machine learning data sets and models is such a problem that you ll find tools from many of the leaders in machine learning development. In ai and machine learning the future resembles the past and bias refers to prior information. In 2019 the research paper potential biases in machine learning algorithms using electronic health record data examined how bias can impact deep learning bias in the healthcare industry. Machine learning a subset of artificial intelligence depends on the quality objectivity and size of training data used to teach it.
Often these harmful biases are just the reflection or amplification of human biases which algorithms learn from training data. In statistics and machine learning the bias variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. Definition of bias variance trade off. A data set might not represent the problem space such as training an autonomous vehicle with only daytime data.
There has been a growing interest in identifying the harmful biases in the machine learning. The article covered three groupings of bias to consider. Bias reflects problems related to the gathering or use of data where systems draw improper conclusions about data sets either because of human intervention or as a result of a lack of cognitive assessment of data.
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