The executive director of the Center for Research for Women’s Action (CIPAF), Syra Taveras, affirmed today that a gender approach is essential in all areas of science and technological development in order to address the high levels of inequality faced by women. In the conference “Digital Inclusion for Gender Equity” organized by the Autonomous University of Santo Domingo (UASD) and the Supérate program of the Social Subsidies Administrator within the framework of the commemoration of International Women’s Day This March, Taveras explained that in the not too distant future, the place that people occupy in society will be determined, mainly, by the level of access to technologies and the skills they have to use them. For this reason, it becomes so important this approach avoids gender bias.
“Gender bias in research, innovation, and technological development consists of assuming gender stereotypes as scientific assumptions, without carrying out a rigorous analysis in terms of sex and gender. So, the gender perspective approach is what allows us to identify, question, and value discrimination, inequality, and exclusion between genders. The practice of ignoring differentiated impacts has had disastrous consequences, ”said Taveras. In this sense, she affirmed that an example of this phenomenon in research and, therefore, in technological innovation is the case of cardiovascular diseases, which were considered for a long time as a masculine condition, for which reason most of the research focused on understanding only how this disease impacts the body of men and, as a consequence, many women were misdiagnosed.
She revealed that another condition is osteoporosis, assuring that this condition has been linked to women and that it is very rare for men to be examined for this disease, however, today, thanks to the application of gender analysis, it has been discovered that the majority of hip fractures suffered by men over the age of 75 are related to it. In addition, the executive director of CIPAF gave an example of sexist algorithms of tools that use AI such as Google or Microsoft translators that interpreted that engineers are men and nurses are women.