Abstract: Methods, non-transitory computer readable media, and causal explanation computing apparatus that assists with generating and providing causal explanation of artificial intelligence models includes obtaining a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation. Next, a plurality of first factors from the disentangled low-dimensional representation of the obtained data that affect an output of the artificial intelligence model is identified. Further, a generative mapping from the disentangled low-dimensional representation between the identified plurality of first factors and the output of the artificial intelligence model, using causal reasoning is determined. An explanation data is generated using the determined generative mapping, wherein the generated explanation data provides a description of an operation leading to the output of the artificial intelligence model using the identified plurality of first factors.

Abstract: Systems and methods for preference and similarity learning arc disclosed. The systems and methods improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and/or ambiguous paired comparisons from subsequent queries The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity.