Bias in machine learning occurs when the artificial intelligence (AI) model makes a prediction that has a systematic error or prejudiced assumption in it. Flawed data and incorrect assumptions create this bias in machine learning. When bias enters into machine learning, it almost always creates incorrect assumptions or places a preference on one dataset over the others. In many cases, a person cannot rely on factual information when a bias occurs in an AI model.
In machine learning, bias appears in several types, including:
Historical bias: The data used for training reflects outdated information, social inequalities, and past prejudices.
Implicit bias: The developer unconsciously adds cultural stereotypes and prejudices.
Sampling bias: The dataset doesn't adequately represent the population for which the AI is meant to be used.
Algorithmic bias: Feature selection, objective function choices, or other design decisions can lead to built-in bias in the algorithm.
Measurement bias: Measured and recorded information introduces errors into the data.
Confirmation bias: Pre-existing beliefs and assumptions are a part of the AI's training.
Representation bias: Bias is created when some groups or information are underrepresented in the training sets.
