A Bibliometric Analysis of Gender Stereotypes in AI

Artificial intelligence became an important part of current learning and work procedures. With its generalization, new challenges emerge such as the formulation of specific ethics rules and intended purposes and the integration of its use in the extant set of generally accepted norms and principles. The present paper integrates in the research area focused on gender stereotypes in artificial intelligence by providing a bibliometric analysis of the articles published in Web of Science on this topic. Using Vos Viewer as analysis instrument, the paper is a structured study of the literature that puts in evidence the main concepts and themes treated so far in this field, the interest shown by the academic community in each of the previous years and in different geographical zones to this topic and the main directions in the evolution of the research on gender stereotypes in artificial intelligence. The paper highlights the importance and interest for this subject and presents a twofold contribution to the literature. By creating a synthesis of the main conclusions of the previous research, it allows a quick understanding of the state of the art for researchers, academics and practitioners in the field. It also provides a basis for developing new research topics related to gender bias and its mitigation strategies in AI, as well as with the integration of this aspect within the research aiming to balance security and data protection in AI within the business environment.