Exploring stereotypes, bias, and expectations of women in the open data context

Abstract

[EN] This study aims to provide a qualitative analysis of the knowledge, beliefs, and perspectives of prominent women working in different sectors in the context of open data. The analysis is based on semi-structured interviews with an outline developed for the interview script. The content obtained was analyzed by attending to categories that deal directly with a gender perspective, covering existing biases, and possible modes of transformation through actions at individual, collective, and institutional levels that incorporate gender sensitivity when working with open data. The findings show that gender bias persists in the process of collecting, analyzing, and interpreting data, as well as in the formation of work groups. The solution, highlighted by all interviewees, involves the development of interoperable and consistent data collection and analysis models that integrate a gender perspective right from the outset, as well as the training of professionals in fields associated with the use of data. Central to this process is the role of government agencies, which should promote efficient public policies giving greater visibility to the roles of women in areas of knowledge associated with open data. Finally, this research also points to the need for innovations such as artificial intelligence to overcome these challenges.