[Purpose/significance] Algorithm explanationis the technical basis of AI governance. Correctly under? standing the difference between algorithm explanation in AI governance and interpretable machine learning in general sense is the key to realizing responsible artificial intelligence (RAI). [Method/process] The requirement characteris? tics of algorithm explanation in AI governance are expoundedfrom three aspects: post- hoc interpretation of AI acci? dents, local interpretation of AI fairness, and algorithm explanation of human-centered. Combined with the governance practices of IBM, Microsoft, Google and Alibaba corporations, the application of algorithm explanation methods is ana? lyzed. [Result/conclusion] Algorithm explanation methods in AI governance include post- hoc interpretation imple? mentation methods represented by feature importance analysis and visual interpretation, local interpretation implemen? tation methods represented by local perturbation methods and counterfactual interpretations, and interpretation meth? ods of multi- objective evolutionary optimization. The commonly used algorithm explanation evaluation methods in? clude human-centered qualitative evaluation, quantitative evaluation based on statistical indicators, and fuzzy evalua? tion based on fuzzy cognition. Finally, the main problems of algorithm explanation in AI governance and the develop? ment direction that should be paid attention to in future research are summarized.