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We present a metric to quantify systematic labeling bias in galaxy morphologydata sets stemming from the quality of the labeled data. This labeling bias isindependent from labeling errors and requires knowledge about the intrinsicproperties of the data with respect to the observed properties. We conduct arelative comparison of label bias for different low redshift galaxy morphologydata sets. We show our metric is able to recover previous de-biasing proceduresbased on redshift as biasing parameter. By using the image resolution instead,we find biases that have not been addressed. We find that the morphologiesbased on supervised machine-learning trained over features such as colors,shape, and concentration show significantly less bias than morphologies basedon expert or citizen-science classifiers. This result holds even when there isunderlying bias present in the training sets used in the supervised machinelearning process. We use catalog simulations to validate our bias metric, andshow how to bin the multidimensional intrinsic and observed galaxy propertiesused in the bias quantification. Our approach is designed to work on any otherlabeled multidimensional data sets and the code is publicly available.

Labeling Bias in Galaxy Morphologies