Although women have increasingly outnumbered men in college enrollment over the years, women have continued to make up only a fraction of data science professionals. The question remains: While the total number of women receiving college degrees increases, what hinders the expansion of diversity in the data science field?
To address this persistent gap, women in career fields that feed into data science have been working to change the circumstances that hinder women from pursuing work in the field, as well urging society to consider the harmful consequences of a field which has historically excluded the voices of women.
Dr. Cecilia Aragon, a major contributor to these ongoing discussions, has endeavored to expand diversity within the field of data science by actively examining the imperfections of the field and considering the benefits of interdisciplinary methodologies, more likely to attract and support diverse data scientists. Her verification of algorithmic bias in computers, for example, has dramatically transformed the field.
Aragon is an award-winning author, airshow pilot and professor—the first Latina to earn the rank of full professor in the College of Engineering at the University of Washington in its 100-year history. Aragon has proven that to combat assumptions in data science, as well as the lack of diversity in the field, a human-centered approach must be prioritized. Her recent publication, Human-Centered Data Science: An Introduction, implores scientists from all fields to demand interdisciplinary approaches in data science, and to make sure that human perspectives are centered. Aragon intends to increase the number of women in data science by advocating for diversity in STEM, and by dismantling harmful hierarchies and binaries in academia.
Aragon’s bold mindset was strengthened by her own confrontation with harmful hierarchies which divided the different academic disciplines she pursued as a student. These hierarchies impact the choices many women make through their academic career, and are reinforced by a lack of diversity, as well as a wide range of harmful, gendered assumptions.
Although Aragon now considers herself a data scientist, there was no “data science” or even “computer science” major when she attended college. However, while pursuing her undergraduate degree in mathematics, she received advice from mentors that computer science was too risky to pursue, and guidance that the field could be a ‘fad.’
Aragon stuck with the field, despite pressure from others. She believed that the value of computer science would persist, and her interest in it allowed her to resist the assumptions and disciplinary hierarchies that worked to separate her from it. This perseverance has characterized her approach in academia, transferring from computer science to the development of Aragon’s current research in data science. It allows her to continue innovating in data science and challenging assumptions.
Today, she realizes the advice she received continues to have relevance for current students, compelling them to doubt their abilities, and holding back the inclusion of diverse voice in data sciences.
One way Aragon challenges the existing hierarchies and inequality in data science and academia is by speaking out against the use of the binary between “soft” and “hard” science. “First of all, I think it’s wrong,” she said when asked to elaborate on this dichotomy.
Women regularly encounter this dichotomy in academia and are more likely to be encouraged to pursue liberal arts as opposed to fields in STEM. Aragon says we should question any information that is oversimplified into a binary. In the case of the ‘hard’ versus ‘soft,” she believes science is a continuum, and in the case of all binaries, she believes that they “impose a hierarchy that doesn’t exist.”
To address these hierarchies, Aragon has found value in incorporating social techniques and applications such as contextual inquiry and ethnography in her research. She believes that the collaboration between data science and other disciplines has the power to drive discovery and to facilitate symbiotic innovation of all fields collaborating with each other.
As well as proposing the use of innovative, interdisciplinary methods in her research, Aragon recognizes the importance of mentorship and collaboration with her students to combat the harmful hierarchies in academia. For Aragon, she believes that working with students keeps her creative and challenges her in the field. On the other hand, working with students allows her to mentor students to pursue academic careers that excite them and to make their own decisions when it comes to their education.
Aragon’s interdisciplinary methods, active inclusion of diverse students with diverse academic backgrounds, and perceptive research questions, all challenge the traditional expectations of data scientists and question the false assumptions of data science. Humans are an essential part of data science, from discovery to curation. Aragon sees the influence that humans hold as a concrete reason to believe that we should be in the loop, as well as a reason to call for representative diversity in the field. If we do not challenge these binaries, they will continue to deter women from considering careers in data science. Aragon’s contributions to data science remind us to invite change, challenge each other, consider fresh perspectives, and to pursue what we are truly passionate about.
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