Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies

Abstract

In the field of communication studies, machine behavior specifically refers to information dissemination activities involving artificial intelligence technology. As algorithms increasingly become the primary force in information dissemination, their potential gender bias becomes increasingly apparent. This paper, based on three research scopes in machine behavior studies: individual behavior, collective behavior, and human-machine interaction behavior, examines the gender bias exhibited by artificial intelligence entities in algorithms at these three levels. At the individual behavior level, the tendency of algorithm development to simplify features overlooks the diversity present in female society. The inherited data bias and human bias make it difficult to avoid gender discrimination. At the collective behavior level, the creation of opinion leader-type social robots expands the subject of information fog, making the concealed gender discrimination against women more covert. The use of large-scale machine armies manipulates search engine results, leading to severe gender bias in search engine outputs. At the hybrid human-machine behavior level, artificial intelligence shapes female images to construct female cognitive thinking. Algorithms acquire human bias during interaction with users, and social robots amplify gender bias issues through mixed human-machine behavior.