Educational work discusses women in science

The Public Ministry of the State of Minas Gerais (MPMG), through the Center for Career Studies and Enhancement (Ceaf), held this afternoon, April 22, the Women in Science Lecture, given by a Master's student in Environmental Science Teaching at the University of São Paulo. (USP), Kananda Iler. The event was opened by Ceaf's Director, Elida de Freitas Rezende, and the person responsible for coordinating the fight against racism and all forms of discrimination (CCRAD), Aleander Barreto.

A graduate in Chemistry from the Federal University of Bahia (UFBA), Cananda is an influential science activist, known for her work promoting black and indigenous representation in science, through lectures and speaking circles at prominent academic and cultural events, such as Unicamp Chemistry Week in 2022, and the SESI Lab in Brasilia in 2024.

During the event, Kananda spoke about her academic career and discussed the limited or no participation of women in management positions in institutions dealing with science. It also showed a graph showing the numbers of PQ-1A research grants in effect in 2023. According to this study, 58.2% of grants were awarded to white men, 29.8% to white women, 0.7% to black men, and 0.0% to men. eggs. Black women.

She discussed a 2018 CNPq survey that showed less than 3% of Black and mixed-race women with doctorates teaching graduate programs. This survey also showed that only 7% of productivity grants are allocated to black women.

Elsewhere, Calandra spoke of a model of science and technology that was created based solely on white characteristics, regardless of racial diversity. He pointed, for example, to methods for detecting skin cancer, based on artificial intelligence, which were unable to identify the disease in black skin.

According to Kananda, this disparity is prompting some people to talk about algorithmic racism, a debate about the data that feeds bots. “This information is entered by people so that robots can identify skin cancer from images. If this database only contains information about white people, the robot will not be able to recognize skin cancer in black people.”