K
Kathleen Martin
Guest
Encouraging women in AI has never been more urgent. A study by the World Economic Forum noted a gender disparity of 78 percent male versus 22 percent female in AI and data science. This disparity isn’t just a challenge within the workforce. It reflects a highly nuanced issue that goes beyond any single workplace and if not addressed will have highly negative implications for society.
We have seen a lot of work to encourage girls and women to become interested in STEM and address gaps in digital skills at an earlier age than in the past. Yet now, there appears to be less effort to support women as they transition from higher education into a sustainable career in tech. This is a challenge for the industry. But the real problem is that as AI becomes ubiquitous in daily life, without a technology workforce that accurately reflects the structure of society, AI-based decisions are constrained by the limited societal and cultural biases of their designers. The impact of such homogeneity in AI decisions and bias has already been seen in examples such as the automation of credit card and mortgage applications, to resume screening and other areas.
The industry challenge is not due to a lack of skills. Research from the Turing Institute suggests women are trailing behind men with industry-relevant skills such as computer science, data preparation and exploration, general-purpose computing, databases, big data, machine learning, statistics, and mathematics. Yet much of this is not due to formal skills, but rather confidence by women in stating these abilities during recruitment and in the workplace. In the tech world where technical skills are needed, soft skills are sometimes dismissed but in order to move forward, there needs to be a greater focus on leadership and mentorship to build confidence and encourage a more diverse workforce. We say that stereotypes must be combatted from a young age yet a gap remains. For example, within the tech sector, women generally have higher levels of formal education than their male counterparts yet academic citations are fewer suggesting there is a lack of confidence in sharing academic knowledge. The Turing Institute finds 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 on machine learning algorithms. The easy-to-understand method employed helped the blog garner over 5 million views, and eventually led to a career in programming. When I became a programmer and found myself working as the only woman in a room of men typically 10-15 years older, I struggled to relate and realized the need for a community of like-minded people.
Continue reading: https://bdtechtalks.com/2022/03/08/encouraging-women-in-tech-is-essential-to-protect-society-against-ai-bias/
We have seen a lot of work to encourage girls and women to become interested in STEM and address gaps in digital skills at an earlier age than in the past. Yet now, there appears to be less effort to support women as they transition from higher education into a sustainable career in tech. This is a challenge for the industry. But the real problem is that as AI becomes ubiquitous in daily life, without a technology workforce that accurately reflects the structure of society, AI-based decisions are constrained by the limited societal and cultural biases of their designers. The impact of such homogeneity in AI decisions and bias has already been seen in examples such as the automation of credit card and mortgage applications, to resume screening and other areas.
The industry challenge is not due to a lack of skills. Research from the Turing Institute suggests women are trailing behind men with industry-relevant skills such as computer science, data preparation and exploration, general-purpose computing, databases, big data, machine learning, statistics, and mathematics. Yet much of this is not due to formal skills, but rather confidence by women in stating these abilities during recruitment and in the workplace. In the tech world where technical skills are needed, soft skills are sometimes dismissed but in order to move forward, there needs to be a greater focus on leadership and mentorship to build confidence and encourage a more diverse workforce. We say that stereotypes must be combatted from a young age yet a gap remains. For example, within the tech sector, women generally have higher levels of formal education than their male counterparts yet academic citations are fewer suggesting there is a lack of confidence in sharing academic knowledge. The Turing Institute finds 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 on machine learning algorithms. The easy-to-understand method employed helped the blog garner over 5 million views, and eventually led to a career in programming. When I became a programmer and found myself working as the only woman in a room of men typically 10-15 years older, I struggled to relate and realized the need for a community of like-minded people.
Continue reading: https://bdtechtalks.com/2022/03/08/encouraging-women-in-tech-is-essential-to-protect-society-against-ai-bias/
