  Feature attribution methods, proposed recently, help users interpret the
predictions of complex models. Our approach integrates feature attributions
into the objective function to allow machine learning practitioners to
incorporate priors in model building. To demonstrate the effectiveness our
technique, we apply it to two tasks: (1) mitigating unintended bias in text
classifiers by neutralizing identity terms; (2) improving classifier
performance in a scarce data setting by forcing the model to focus on toxic
terms. Our approach adds an L2 distance loss between feature attributions and
task-specific prior values to the objective. Our experiments show that i) a
classifier trained with our technique reduces undesired model biases without a
trade off on the original task; ii) incorporating priors helps model
performance in scarce data settings.
