The Challenge of Neutral Algorithms

In theory, algorithms are neutral, but in practice, they can perpetuate gender stereotypes due to the data they are fed. Data collection processes and historical information often contain biases that can skew algorithmic decisions, reinforcing societal norms and stereotypes rather than challenging them. For example, credit scoring algorithms that use historical financial data might disadvantage women, reflecting past inequalities rather than current realities or future potentials.

In theory, algorithms are neutral, but in practice, they can perpetuate gender stereotypes due to the data they are fed. Data collection processes and historical information often contain biases that can skew algorithmic decisions, reinforcing societal norms and stereotypes rather than challenging them. For example, credit scoring algorithms that use historical financial data might disadvantage women, reflecting past inequalities rather than current realities or future potentials.

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Bias in AI and Algorithms

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