Human knowledge may be injected in an explainable AI system in order to improve the model's generalization error, model accuracy, interpretability of the model, avoid or eliminate bias, while providing a path towards the integration of connectionist systems with symbolic logic in a combined AI system. Human knowledge injection may be implemented by harnessing the white- box nature of explainable/interpretable models. In one exemplary embodiment, a user applies intuition to model-specific cases or exceptions. In another embodiment, an explainable model may be embedded in workflow systems which enable users to apply pre-hoc and post-hoc operations. A third exemplary embodiment implements human-assisted focusing. An exemplary embodiment also presents a method to train and refine explainable or interpretable models without losing the injected knowledge defined by humans when applying gradient descent techniques. The white-box nature of explainable models allows for precise source attribution and traceability of knowledge incorporated into the model.