In the technology industry, we talk a lot about the lack of women in technical or leadership roles. In cybersecurity, the issue is even more precarious and dire. As you might guess, things get worse as you move into more niche positions. In data science, for instance, it can be even tougher for a woman to break into the business, no matter how talented and qualified she is.
While it’s thrilling and inspiring to see all the work going into getting more young girls into science, technology, engineering and math (STEM) programs, we also have to also consider the unconscious (and, sadly, probably conscious) bias of leaders and recruiters seeking new talent to fill technical roles.
In my experience, it’s rather difficult to be taken seriously as a woman doing research topics involving mathematics and statistics. There is a general unconscious bias that many of us have, myself included, that tends to take masculine people at their word when it comes to analytical results, where we may more likely question the same results if presented by a woman.
The assumption goes that men know what they’re talking about, whereas women might not fully grasp the numbers, algorithms, and complexity they’re dealing with. It’s usually unconscious and entirely unintended, but it’s a real, measurable effect and it IS sexism.
A Case Study In Doubt
In my own career as a technical female, I have seen many examples of this, but one in particular experience in my past makes for an interesting case study.
As part of a larger machine-learning study at a previous employer, I needed to solve (Read more...)
This is a Security Bloggers Network syndicated blog post authored by Hailey Buckingham. Read the original post at: Cylance Blog