Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

By Giorgio Visani, Enrico Bagli, Federico Chesani, A. Poluzzi, Davide Capuzzo. 2020

219 citations recorded by the source corpus.

Abstract Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.

Subfields: Accounting, Artificial Intelligence, Finance

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “…number of shortcomings to this approach around, for instance, the generation of data or the loss of accuracy, which have been addressed with enhancements (Botari et al. 2020; Visani et al. 2020; Zhang et al. 2019), or with alternative approaches (Lundberg et al. 2020, 2018; Lundberg and Lee 2017).”