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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 modeldoi:10.18653/v1/p19-1631 dblp:conf/acl/LiuA19 fatcat:xwjz46gsgfh2xftmrhkehyczwe