Current Machine Learning model evaluation methods, e.g., the use of test sets, will only detect whether a model’s predictions match the data. They cannot exclude the possibility that both predictions and data are biased. More targeted efforts to reduce or eliminate training data biases require either manual adjustments of the models, domain knowledge, or assume that data or model issues can easily be identified. This is seldom the case. Model auditing generally relies on manual analysis of the models, on existing data, and knowledgeable auditors. Automatically identifying deficiencies in both data and training, and how they impact application of models, is still an unsolved question. Bias mitigation approaches require either the ground-truth distribution or concrete information on the bias — or unbiased / differently biased data from other sources. In this project, we aim to develop a model-agnostic framework that will not be limited by any of these requirements.