How to Close the Gender Gap in Data and Computer Science? – Despite widespread efforts to remove barriers and make changes to the status quo, a stark employment gap remains between genders in many traditionally masculine professions and industries. The data and computer science field is no exception. A stubborn deficit exists between the number of women employed in this discipline as compared to men. However, as more and more players within the landscape become aware of this reality and work to change it, overturning the gender gap seems more possible than ever.
Gender Representation in Computer Science Jobs
The current employment rate by gender within the data and computer science industry reveals persistent inequalities between men and women. In the United States, only 1 in 4 computing jobs are held by women. In higher education, women earn less than 20% of the computing degrees issued each year. In fact, some suggest that female presence in the computing industry has actually diminished somewhat over the last four decades rather than increased.
To understand the computer science climate and address its gender gap, it’s important to start at the source. Educational statistics reveal that the gender disparity begins much earlier than college, careers, or entering the workforce. To make meaningful change in the industry, effort must first be made to change the climate of K-12 education. Studies that dissect the messages children receive regarding the viability of a career in computer science have found that:
Girls are not encouraged to consider or pursue careers in computer science nearly as often as boys.
Adults and parents do not support or suggest computer science careers to girls as often as they do to boys.
Though girls perform just as well in computer science education as boys do, girls statistically lack confidence in their ability to perform in computer science classes and fields as compared to boys.
To create meaningful change in the industry, we must change the messages and systems that form and inform young children before they reach decision points that will determine their career trajectories (what classes they’ll take in high school, what degree they’ll earn in college, and what jobs they’ll pursue upon graduation). Science, Technology, Engineering, and Mathematics (STEM) must become accessible, encouraged, and supported for girls just as much as it already is for boys.
Current Initiatives to Change the Status Quo
Fortunately, important work in this area is being accomplished by a huge number of organizations, regulatory bodies, and initiatives. One way this is done is through STEM-specific programs and offerings for elementary and middle school-aged girls both within school systems and through extracurricular programming. Educational organizations, nonprofits, higher education institutions, women’s empowerment groups, and more conduct classes, events, and interventions across the country and around the world that help girls learn computer science skills, connect with like-minded peers, and find female mentors and professionals in the industry.
Government-backed programs create another avenue of work in this area that make it possible for girls to get involved in STEM and computer science activities. Many states have created their own female-centric STEM initiatives that are implemented through educational systems or other outlets, and Federal entities like NASA and others have created their own programs or partner with/sponsor activities that do the same.
Reasons Why More Women Should Enter the Data and Computer Science Field
For some of us, the question remains: Why is this endeavor so important? The answer is multifaceted. There are a few important ways that a more gender-equal computer science field would significantly benefit not just those who enter the field but the rest of the world as well.
First, data science can be used for tremendous social good. In today’s technological age, data science is involved in some element of almost any initiative. To diversify the professional workforce handling that data would improve the outcomes it can make for everyone.
Second, every aspect of the way our world works is increasingly informed and dictated by big data. Social media and behavior prediction algorithms, the internet of things, the increasing use of machine learning and artificial intelligence, virtual technology, cryptocurrencies, and more – data informs all of it, and its influence will continue to increase. Because of this, it’s vital that the entirety of the world’s population is more accurately reflected in the teams that create those data systems. Biases are inevitable when decision-makers in any sphere of data or computer science don’t include portions of the population those decisions will affect. And when women aren’t represented in those processes, results will unconsciously reflect male experiences. This is almost never done maliciously. It is simply unavoidable when other voices aren’t included.
Third, just as any industry does, computer science benefits from greater diversity in thought, influences, and decision-making. Obviously, this can be accomplished by increasing the diversity of the thinkers, influencers, and decision-makers embedded in the industry. When women pursue positions within the data and computer science field, they not only benefit all females by ensuring that the products and systems that computer science creates are more equally amenable, but benefit the industry itself by infusing it with greater diversity of thought and a richer fabric of experience from which innovative solutions can be fashioned.
Finally, computer science boasts one of the smallest gender pay gaps in today’s professional landscape. According to recent salary comparisons, women in data and computer science positions make 94% of what men in comparable positions make. While this is still not equitable, this figure is much better than in some industries.
By supporting existing initiatives, creating new ones, or considering entering the field yourself if you are a female, we can all contribute to closing the gender gap in data and computer
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