Abstract: Provided are methods, systems, and computer-storage media for developing machine learning technology that is less susceptible to bias problems. A machine learning model may be developed with reduced error attributed to one or more sensitive features by utilizing a loss adjustment weight to determine an adjusted loss function used to train the model. The loss adjustment weight may be determined based on a count of a feature-label combination of a sensitive feature. The adjusted loss function is determined and configured to use the loss adjustment weight when determining loss during model training, and the output of the adjusted loss function is an adjusted loss. The machine learning model may be trained until the adjusted loss satisfies a loss threshold, indicative of an acceptable level of model inaccuracy. Accordingly, present embodiments can provide use case specific tailoring to improve machine learning systems by removing biases associated with certain data features.