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A commonly observed pattern in machine learning models is an underpredictionof the target feature, with the model's predicted target rate for members of agiven category typically being lower than the actual target rate for members ofthat category in the training set. This underprediction is usually larger formembers of minority groups; while income level is underpredicted for both menand women in the 'adult' dataset, for example, the degree of underprediction issignificantly higher for women (a minority in that dataset). We propose thatthis pattern of underprediction for minorities arises as a predictableconsequence of statistical inference on small samples. When presented with anew individual for classification, an ML model performs inference not on theentire training set, but on a subset that is in some way similar to the newindividual, with sizes of these subsets typically following a power lawdistribution so that most are small (and with these subsets being necessarilysmaller for the minority group). We show that such inference on small samplesis subject to systematic and directional statistical bias, and that this biasproduces the observed patterns of underprediction seen in ML models. Analysinga standard sklearn decision tree model's predictions on a set of over 70subsets of the 'adult' and COMPAS datasets, we found that a bias predictionmeasure based on small-sample inference had a significant positive correlations(0.56 and 0.85) with the observed underprediction rate for these subsets.

Systematic Bias in Sample Inference and its Effect on Machine Learning