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

Understand

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.