A Model-Agnostic Framework for Interpretable Electricity Theft Detection

Although machine learning models have been widely used in electricity theft detection, most of them lack interpretability, which hinders user trust and policy enforcement. To this end, this paper aims to investigate the interpretability of machine learning models in electricity theft detection. Specifically, a comprehensive theoretical analysis is conducted to reveal why the interpretability is needed in electricity theft detection. Then, a model-agnostic explainable artificial intelligence (XAI) framework is proposed to uncover the potential start and end times of fraudulent behavior, and to clarify the rationale behind identifying fraudulent users within machine learning models by calculating the importance score of each data point. Simulation results demonstrate that the XAI framework provides class-discriminative data points to interpret fraudulent activities, enabling suspicious users to understand why the machine learning model identified them as suspicious and guiding model improvement. Moreover, compared with benchmarks (e.g., Shapley additive explanations, local interpretable model-agnostic explanations, and gradient-weighted class activation mapping techniques), the harmonic mean of overlap and coverage (HMOC) of the proposed XAI framework is improved by 10.26% to 54.73%, indicating more trustworthy interpretations.