Abstract: Generative AI has triggered a transformation in the paradigms of knowledge production and dissemination across various fields. Meanwhile, biases such as "favoring males over females" and "discrimination against minorities" have been laid bare. Previous research on the ethics of technology has largely been speculative, with few empirical studies following the social constructivist approach and focusing on the role of humans, while insufficient attention has been paid to non-human actors. In light of this, the study employs actor-network theory to examine the labor process of generative AI data annotators, exploring how human and non-human actors intertwine to inscribe biases into intelligence during the practical construction of these biases. The research identifies three causes of bias production: "individual behavior," "constraints of objects," and "capital orientation." Finally, the study offers suggestions at the end for managing biases in generative AI through "identification discussions," "process optimization," and "ethical frameworks."