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Interpretable Machine Learning (IML) is expected to remove significantbarriers for the application of Machine Learning (ML) algorithms in powersystems. This letter first seeks to showcase the benefits of SHapley AdditiveexPlanations (SHAP) for understanding the outcomes of ML models, which areincreasingly being used. Second, we seek to demonstrate that SHAP explanationsare able to capture the underlying physics of the power system. To do so, wedemonstrate that the Power Transfer Distribution Factors (PTDF) -- aphysics-based linear sensitivity index -- can be derived from the SHAP values.To do so, we take the derivatives of SHAP values from a ML model trained tolearn line flows from generator power injections, using a simple DC power flowcase in the 9-bus 3-generator test network. In demonstrating that SHAP valuescan be related back to the physics that underpin the power system, we buildconfidence in the explanations SHAP can offer.

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations