The notion that women are not qualified to take up positions in science and technology-related industries is — to put it mildly — utter bologna.  

As a twenty-something female with two liberal arts degrees and almost one-year of experience under my belt in the technology industry — it's OK, I'm just as shocked to be writing these words as you are to be reading them — I think it's safe to say that I know nothing about everything, but I also know a lot about some things.

And in my first year, if I have learned anything to be true it is that one, y'all need to chill with the acronyms, but I'll revisit that sentiment in another story. And two, technology is made by the people for the people to whatever capacity we choose to use it. It is a reflection of all of us — or, at least it would be if there was equal representation of women and people of color in the industry.

Very fluffy perspective, I know. But cut me some slack, I'm new. The rose-colored perspective will fade as I'm sure the gender gap will, too. But I digress.

If it's traditional engineering and computer science degrees that companies want to ensure competence and consideration of female applicants, then it delights me to share the latest data from the National Science Foundation. More women than ever are earning science, technology, engineering, and mathematics (STEM) degrees – and they are catching up to men in earning bachelor’s degrees in science and engineering subjects.

So why are women in technology still vastly underrepresented?

During Chef’s "WomXn in Tech Panel: Success in the Face of Adversity" event last week, each of the six female panelists, whose titles ranged from developer advocate to automation engineer, shared their personal stories of how they conquered the obstacles and adversity in their professional lives. 

Their stories covered the full gamut of challenges women experience in the industry from being silenced and ignored to facing sexist bosses and coworkers. And no matter the difference in sector or position, there was a resounding theme of similarity stitching each individual experience: gender bias. 

Landing a job in a male-dominated industry is one thing, but finding the strength to stay is another. It’s not easy for the majority of women working in male-dominated environments when their career experience, including recruitment, performance evaluation, salary levels, mentoring, and career development opportunities are limited because of gender. These problems start at a young age where misconceptions about technology can influence some women to overlook the opportunities available to them in the digital world.

Benny Vasques, community manager at Chef, said getting rid of the “well that’s for men” and “I can’t do that” rhetoric early on is important in stopping young girls from ignoring their interests and limiting their potential.

That's not even a hard ask. If we championed young girls' interest in the sciences the same way we gush over toddlers in tiaras there is not a doubt in my mind that women would be running the industry by now. So in keeping to the nature of Vasques' simple sentiment to foster change, I'll try and make my message to parents and educators just as simple: Get out of her way.

In the past few years the world has seen self-driving cars — and quite literally a number of senseless car accidents as a result — robotic gas pumps, appliance-controlling adapters, automated snore stopping pillows, and electronically automated doggy doors, just to name a few.

Despite all of their differences, tech giants Google, Amazon, Facebook, Apple, and Microsoft all agree on a future dominated by smart machines such as artificial intelligence (AI) and machine learning. 

But they can’t get there without diversity. 

Hiring women and people of color are no longer just nice words to put in marketing materials, they are business imperatives.

AI maturity and selection bias are two of the biggest challenges that enterprises face in advancing machine learning technologies, and both are problems that could be fixed by simply hiring more women. 

As Mark Minevich writes in a Forbes article, both corporate and academic AI teams have already released data and systems biased against people poorly represented among the high priests of AI.

Minevich cites the findings from researchers at the universities of Virginia and Washington just last year that showed that two large image collections used in machine learning research, including one backed by Microsoft and Facebook, teach algorithms a skewed view of gender. Images of people shopping and washing are mostly linked to women.

"In order for organizations to achieve the highest AI maturity levels, it is necessary to mobilize women on a mass scale and include them as part of all enterprise endeavors in artificial intelligence, from research to product launch,” he wrote.  

Perhaps the industry would recognize that its table is half empty and that the technology we interact with daily could be a better representation of the people that use it — which, as I'm sure you're aware, is everyone, not just white dudes with an affinity for overpriced beer — if they would just quit manspreading.