How Shattering Stereotypes Can Save Tech
Right now there are currently over 500,000 unfilled technology related jobs in the United States. By 2020 there will be 1 million unfilled jobs. But why are there so many unfilled jobs? The reason is because we are missing half of the population, such as woman and people of color.
“Stereotypes are familiar to all of us,” said Jane Margolis, SR Researcher at UCLA. “The stereotypes of someone in tech are mostly white and asian males. People have this notion that people like this were born to do it.” While Margolis was studying highschools in Los Angeles, she noticed an interesting pattern. In the wealthy schools, the students have full opportunities to learn computer science. On the other hand, schools with higher numbers of latinos and African-Americans only teach word processing and internet searching in place of computer science courses. “When we talked to the principals, they said their students were not interested in this stuff. They have these stereotypes that African-Americans and Latinos aren’t the ‘type’ to learn these things, so they didn’t offer the learning opportunities. I was very mad when I learned of this.”
Robin Hauser Reynolds, director/producer of Finish Line Features LLC, notes how these stereotypes start young with gender bias. “We grew up with biases and continue them today. We send girls down the toy aisle full of pink things and dolls, and it’s not changing. But it needs to,” Reynolds said.
“I think stereotypes serve as a barrier on both sides and influence who’s in and who’s out. Who gets access in spaces and who belongs in them,” Brown said. “It’s not all about Silicon Valley, what about the other places? Can we start tapping into the talent in other communities? If I had to put money on something, it wouldn’t be on the big groups in Silicon Valley, it would be on the small startups starting to emerge.”
Reynolds was interested to explore if there was a difference in the brain that would make someone a better coder (female vs male). What she found out was interesting. A female brain scientist confirmed that there is no difference, but that what would make someone a better coder relied on the experience and access you have to that education. From the beginning everyone has the same equal ability to learn. She was concerned people would be skeptical with the facts coming from a female, so she got a male brain scientists to do some research too. The male brain scientists also confirmed that there was no difference.
An experiment was created using a situation where people were choosing code to use from GitHub without knowing the gender of the coder. 78.6% of the code that was written by women was accepted when the gender was not identified. People chose women’s code before they knew the gender.