Explanation Shift: How Did the Distribution Shift Impact the Model?

• URL: http://arxiv.org/abs/2303.08081v2
• Date: Thu, 7 Sep 2023 17:04:12 GMT
• Title: Explanation Shift: How Did the Distribution Shift Impact the Model?
• Authors: Carlos Mougan, Klaus Broelemann, David Masip, Gjergji Kasneci, Thanassis Thiropanis, Steffen Staab
• Abstract summary: We study how explanation characteristics shift when affected by distribution shifts. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.
• Score: 23.403838118256907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As input data distributions evolve, the predictive performance of machine learning models tends to deteriorate. In practice, new input data tend to come without target labels. Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions. We suggest a novel approach that models how explanation characteristics shift when affected by distribution shifts. We find that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state-of-the-art techniques. We analyze different types of distribution shifts using synthetic examples and real-world data sets. We provide an algorithmic method that allows us to inspect the interaction between data set features and learned models and compare them to the state-of-the-art. We release our methods in an open-source Python package, as well as the code used to reproduce our experiments.

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