Machine learning bias, also known as algorithm bias or artificial intelligence bias, is a phenomenon that happens when an algorithm generates results that are systematically biased as a result of false assumptions made during the machine learning process.
What is machine learning bias (AI bias)?
- Artificial intelligence (AI) has several subfields, including machine learning. Machine learning relies on the caliber, objectivity, and quantity of training data.
- The adage "garbage in, garbage out" is often used in computer science to convey the idea that the quality of the input determines the quality of the output. Faulty, poor, or incomplete data will lead to inaccurate predictions.
- Most often, issues brought on by those who create and/or train machine learning systems are the source of bias in this field. These people may develop algorithms that reflect unintentional cognitive biases or actual prejudices.
- Alternately, the people could introduce biases by training and/or validating the machine learning systems using incomplete, inaccurate, or biased data sets.
- Stereotyping, bandwagon effect, priming, selective perception, and confirmation bias are examples of cognitive biases that can unintentionally affect algorithms.
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