Gopher is a system that produces compact, interpretable and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior. It generates the top-𝑘 patterns that explain model bias that utilizes techniques from the ML community to approximate causal responsibility and uses pruning rules to manage the large search space for patterns.