What is proxy label bias?

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Multiple Choice

What is proxy label bias?

Explanation:
Proxy label bias happens when you train a model to predict a stand-in target rather than the real outcome, and that stand-in carries inequities. Because the model’s objective is to get the proxy right, it ends up learning patterns tied to biased or unequal information in the proxy itself. If the proxy encodes disparities related to protected attributes, the model will reflect and perpetuate those inequities in its predictions, even if the true target would lead to fairer decisions. Using the true outcome directly avoids this bias path, but if a proxy must be used, it’s important to scrutinize whether the proxy itself embeds inequities and to apply fairness-aware methods to mitigate that effect. By contrast, data leakage involves information the model shouldn’t have during prediction, and overfitting is about memorizing training data rather than learning from a biased target.

Proxy label bias happens when you train a model to predict a stand-in target rather than the real outcome, and that stand-in carries inequities. Because the model’s objective is to get the proxy right, it ends up learning patterns tied to biased or unequal information in the proxy itself. If the proxy encodes disparities related to protected attributes, the model will reflect and perpetuate those inequities in its predictions, even if the true target would lead to fairer decisions. Using the true outcome directly avoids this bias path, but if a proxy must be used, it’s important to scrutinize whether the proxy itself embeds inequities and to apply fairness-aware methods to mitigate that effect. By contrast, data leakage involves information the model shouldn’t have during prediction, and overfitting is about memorizing training data rather than learning from a biased target.

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