A phenomenon known as "machine learning bias," often referred to as "algorithm bias" or "artificial intelligence bias," occurs when an algorithm produces results that are routinely prejudiced as a result of incorrect assumptions established during the machine learning process.
In AI, bias comes in two flavors. One type is "data bias," in which algorithms are taught on skewed data. The second type of AI bias is societal AI bias.
Input bias, training bias, and programming bias are the three basic causes of algorithmic prejudice. 20 In contrast, algorithmic results that are frequently referred to as "biased" may merely reflect unfavorable truths based on causal links drawn from trustworthy representative data.
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