Noise Induces Loss Discrepancy Across Groups for Linear Regression
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We study the effect of feature noise (measurement error) on the discrepancy between losses across two groups (e.g., men and women) in the context of linear regression. Our main finding is that adding even the same amount of noise on all individuals impacts groups differently. We characterize several forms of loss discrepancy in terms of the amount of noise and difference between moments of the two groups, for estimators that either do or do not use group membership information. We then study how long it takes for an estimator to adapt to a shift in the population that makes the groups have the same mean. We finally validate our results on three real-world datasets.
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