Machine Learning Bias Definition

Machine bias is also known as algorithm bias or simply bias. Machine learning is a branch of artificial intelligence AI and computer science which focuses on the use of data and algorithms to imitate the way that humans learn gradually improving its accuracy.

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IBM has a rich history with machine learning.

Machine learning bias definition. 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. Nearly all of the common machine learning biased data types come from our own cognitive biases. Machine bias is the effect of erroneous assumptions in machine learning processes.

The bias of a specific machine learning model trained on a specific dataset describes how well this machine learning model can capture the relationship between the features and the targets. One of its own Arthur Samuel is credited for coining the term machine learning with his research PDF 481 KB. Bias is one type of error which occurs due to wrong assumptions about data such as assuming data is linear when in reality data follows a complex function.

Some examples include Anchoring bias Availability bias Confirmation bias and Stability bias. To achieve this the learning algorithm is presented some training examples that demonstrate the intended relation of input. So for our example the bias of any one model would tell us how well this particular model can predict the exam points received for any number of hours studied in our specific dataset.

Bias machine learning can even be applied when interpreting valid or invalid results from an approved data model. AI bias is an anomaly in the output of machine learning algorithms. What are the types of AI bias.

The inductive bias also known as learning bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. This also is one type of error since we want to make our model robust against noise. Bias in Machine Learning is defined as the phenomena of observing results that are systematically prejudiced due to faulty assumptions.

In machine learning one aims to construct algorithms that are able to learn to predict a certain target output. What is machine learning. He defined it to mean that a learning algorithm will not generalize unless it introduces some form of preference or restriction over the space of possible functions.

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. On the other hand variance gets introduced with high sensitivity to variations in training data. In his 1980 paper entitled The need for bias in learning generalizations Tom Mitchell introduced the first use of the word bias in machine learning.

AI systems contain biases due to two reasons. These could be due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data.

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