Bias, simply put, is an unfair preference or bias toward one person or group over another. It can lead to unequal treatment or a lack of fairness in decision-making. In the context of artificial intelligence and machine learning, bias occurs when the computer system makes decisions based on the data on which it has been trained that consistently favor one group or outcome over another, reflecting existing inequalities or stereotypes.

Examples of such biases include AI-powered law enforcement software that recommends longer prison sentences for black offenders than for white offenders for similar crimes, or facial recognition software that recognizes white faces better than black faces. These shortcomings are often due to social inequalities in the training data used by these systems. Today’s AI systems function primarily as pattern replicators, processing large amounts of data using neural networks to recognize patterns. If the training data contains an imbalance, such as a higher number of white faces compared to black faces, or historical sentencing data showing a disparity between black and white offenders, machine learning systems may inadvertently learn and perpetuate these biases, automating inequities.

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