Empirical Knowledge Embedded Algorithm Decision-Making: Model Construction and Public Value Realization of Human-Computer Collaborative Decision-Making

The continuous breakthroughs in artificial intelligence technology have accelerated the process of intelligent government decision-making. Is experience and knowledge no longer available? Focusing on the debate on government decision-making models under the background of artificial intelligence technology, this paper discusses the advantages and limitations of the three paths of algorithmic decision-making, empirical decision-making and human-machine collaborative decision-making. It points out that at a time when decision-making scenarios are becoming increasingly complex and data resources are far from sufficient, human-machine collaborative decision-making is more applicable, and an application matrix of decision-making models under the complexity of data resources and decision-making scenarios is proposed. By exploring the decision-making logic of human-computer collaboration, this paper analyzes the connotation and method of embedding empirical knowledge, that is, using social science measurement methods to order empirical knowledge into data, and then establishing a mapping relationship with unbiased big data to form a government decision-making model architecture. This links empirical theory and data technology to ensure that data representing the temperature of public value is deduced along with the algorithm model.