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Explainable AI by combining statistical and logical methods

Explainable AI aims to solve the transparency problem of AI, such that AI systems address how black box decisions are made. It inspects and understands each step of reasoning procedure. The combination of statistical learning and logical methods is one potential direction of solving AI interpretability. Statistical learning analyses data distribution together with explainable machine learning models, which gives explanation for the outputs of AI black box. Meanwhile, logical rules and reasoning handle the interpretability of AI decisions. Through well-designed learning methods, statistical and logical methods learn the latent meanings of AI systems and models so that interpretability can be achieved.