[Purpose/significance] In view of the bias and discrimination in the application of artificial intelligence, this paper analyzes the influencing factors and evaluation methods of fairness in AI governance from the perspective of data, algorithms and system operation and maintenance, and provides solutions for the fairness governance of AI. [Method/process] Based on the two dimensions of AI governance process and AI fairness governance mechanism, this paper first follows the idea of "Top-level design-Technical research- Industrial practice", and proposes the fairness governance framework of AI; Secondly, at the technical level of AI governance fairness,the GovAI-FDevOps framework is proposed, the influencing factors and quantitative evaluation indicators of fairness are discussed from the perspective of bias; Finally, taking the credit data set German as an example, this paper tests and evaluates the fairness of the credit scoring card model in gender, nationality, age and other sensitive attributes. [Result/conclusion] From the research of this paper, fairness in AI governance is a complex concept with relativity, and there is no absolute fairness that satisfies all groups and statistical significance. The realization of AI governance fairness requires the concerted efforts of policy, law and technology.