Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation
This work addresses the need for interpretability in deep learning systems, particularly for users requiring explanations, but it appears incremental as it builds on existing synergies between deep learning and case-based reasoning.
The paper tackles the problem of Explainable AI (XAI) in deep learning by pairing opaque deep learning models with transparent case-based reasoning models to generate factual, counterfactual, and semi-factual explanations, and suggests applications for data augmentation.
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we report on a program of research that applies CBR solutions to the problem of Explainable AI (XAI) in the DL. We describe a series of twin-systems pairings of opaque DL models with transparent CBR models that allow the latter to explain the former using factual, counterfactual and semi-factual explanation strategies. This twinning shows that functional abstractions of DL (e.g., feature weights, feature importance and decision boundaries) can be used to drive these explanatory solutions. We also raise the prospect that this research also applies to the problem of Data Augmentation in DL, underscoring the fecundity of these DeepCBR ideas.