Historical bias refers to the tendency for decisions, perceptions, and data interpretations to be influenced by the historical context in which they occur, often leading to skewed outcomes or misrepresentations. This concept is critical when considering AI and algorithmic decision-making, as historical biases embedded in training data can perpetuate discrimination or reinforce existing inequalities in society.
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