Encouraging women in AI has never been more urgent.
This disparity isnt just a challenge within the workforce.
This is a challenge for the industry.
The industry challenge is not due to a lack of skills.
We say that stereotypes must be combatted from a young age yet a gap remains.
TheTuring Institutefinds that only 20 percent of UK data and AI researchers on Google Scholar are women.
Of the 45 researchers with more than 10,000 citations, only five were women.
When I say that women need to have mentors and role models, I write from firsthand experience.
It was only after winning a mathematics modeling competition in university that I considered a related career.
This inspired me to write a blog onmachine learning algorithms.
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In 2021, open-source component downloads grew 73 percent YOY.
She sought advice from more senior programmers and tech leaders.
It is experiences like this that will keep women in tech.
When they stay, tech also benefits.
But encouraging women isnt simply about creating diversity within the industry to enable greater gender balance.
The benefits stretch beyond the sector and into the societal benefits.
It is only by broadening the pool of talent that we can avoid data-leddecisions skewed by bias.
Establishing communities that actively foster participation and diverse voices is an important step.
Bias in AI starts with the initial formulation of problems.
The questions are naturally constrained by the experiences of the designers and programmers.
This in turn impacts the quality of the data and the way it is handled.
So what will be the societal impact if there is not greater diversity?
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