  A wide variety of fairness metrics and eXplainable Artificial Intelligence
(XAI) approaches have been proposed in the literature to identify bias in
machine learning models that are used in critical real-life contexts. However,
merely reporting on a model's bias, or generating explanations using existing
XAI techniques is insufficient to locate and eventually mitigate sources of
bias. We introduce Gopher, 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.
Specifically, we introduce the concept of causal responsibility that quantifies
the extent to which intervening on training data by removing or updating
subsets of it can resolve the bias. Building on this concept, we develop an
efficient approach for generating the top-k patterns that explain model bias
that utilizes techniques from the machine learning (ML) community to
approximate causal responsibility and uses pruning rules to manage the large
search space for patterns. Our experimental evaluation demonstrates the
effectiveness of Gopher in generating interpretable explanations for
identifying and debugging sources of bias.
