What is disparate impact?

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Multiple Choice

What is disparate impact?

Explanation:
Disparate impact is about the effects of a policy or model, not the intent behind it. It occurs when a policy that is neutral on its face ends up producing adverse outcomes that disproportionately affect members of a protected group. The key idea is that fairness concerns arise from the actual impact on groups, even if no one involved intended to discriminate. Think of a hiring test or a screening rule that, while seemingly fair, ends up approving far fewer applicants from a protected group because of hidden biases in the data or design. That unequal outcome is the hallmark of disparate impact. It’s important to recognize because it can be unlawful and it signals that the process should be examined and adjusted to reduce the unequal effects. Deliberate discrimination, by contrast, is about purposeful bias toward or against a protected characteristic. Equal outcomes across all groups would mean there’s no disparate impact. A model’s misclassification rate focuses on general accuracy, not specifically on whether its effects fall unevenly across protected groups.

Disparate impact is about the effects of a policy or model, not the intent behind it. It occurs when a policy that is neutral on its face ends up producing adverse outcomes that disproportionately affect members of a protected group. The key idea is that fairness concerns arise from the actual impact on groups, even if no one involved intended to discriminate.

Think of a hiring test or a screening rule that, while seemingly fair, ends up approving far fewer applicants from a protected group because of hidden biases in the data or design. That unequal outcome is the hallmark of disparate impact. It’s important to recognize because it can be unlawful and it signals that the process should be examined and adjusted to reduce the unequal effects.

Deliberate discrimination, by contrast, is about purposeful bias toward or against a protected characteristic. Equal outcomes across all groups would mean there’s no disparate impact. A model’s misclassification rate focuses on general accuracy, not specifically on whether its effects fall unevenly across protected groups.

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