Gender Disparity in Data Science and Artificial Intelligence

Gender Disparity in Data Science and Artificial Intelligence

Technology research is seeing a rapid increase. Machine learning and data science have captured the attention of the world. Given the rapid expansion of the IT sector in every field, most future jobs will require sound computer science knowledge. However, gender-based disparities exist in the tech industry, especially in data science and artificial intelligence professionals. Female professionals are much under-represented in such sectors. The 2020 Kaggle Machine Learning and Data Science survey had 20,000 responders; only 20% were women. Low representation isn't the only issue female professionals face in data science and artificial intelligence. Read on to find out how gender disparity exists in these fields:

1.    Salary Differences

The US Census Bureau estimates that women make $0.81 on average compared to each dollar by men. This substantial gender-based pay gap exists despite any difference in skills and responsibilities of women compared to their male counterparts. The median salary in the United States for women data scientists is $90,000 to $100,000, while the men's median salary is $100,000 to $125,000. These salary differences lead to low employee motivation and unfair workplace practices, discouraging more women from entering the industry.

2.    Educational Background

While most women and men who work in the big tech and artificial intelligence industry have similar education up to their bachelor's, more men go for higher education than women. Data science employees have various degrees: 51% of them hold bachelor's degrees, 34% have a graduate degree, and 13% hold doctoral degrees.

However, women Ph.D. holders are a minority within a minority: the Stanford 2021 AI Index has shown that less than 19% of women are included in North America's AI and computer science Ph.D. programs. Few women pursue higher education in this field, contributing to less research and development of technologies than their male counterparts. Most women leave their educational pursuits due to increased family responsibilities, pregnancies, unaffordability of higher education, and sexism in awarding scholarships or grants. This educational disparity results in fewer women researching data science or getting managerial positions in private firms.

3.    Lack of Promotional Opportunities

As women enter this profession and strive to climb the corporate ladder, managerial-level opportunities for women are limited. Women have some representation at the lower ranks, but few make it to the top of the hierarchy.

According to a study by Turing Institute, which used data from over 19,500 LinkedIn profiles of data science and AI professionals, women report 30 or fewer skills on their LinkedIn profiles. Only 12% of women professionals had added over 45 skills to their profile, while more than 16% of men reported having more than 45 skills. Women are relatively new to these traditionally male-dominated professions and lack the confidence to promote their qualifications and skill level. This confidence gap leads them to fewer opportunities for managerial and c-level jobs.

While women make up about 45% of total employees in public companies across the United States of America, only 20% are board members, and just 5% of them hold CEO jobs. According to Turing, women tend to leave their data science profession mid-career due to family commitments, pregnancies, or relocation. Lack of maternity leave can stall women's promotions or result in losing their jobs. Inaccessibility of daycare services or compensation from employers also leads women to quit their careers or take prolonged career breaks. This can further discourage women and lead to low women participation in data science and emerging technologies.

How Can Data Science and AI Become More Inclusive?

Women's empowerment and inclusion in data science and AI are essential. AI firms can contribute by cultivating a fair and safe work culture, implementing paid maternity leave, and indulging in activism for inclusion and diversity in data science. State-run high school coding competitions should encourage young girls to participate. Scholarships and grants for women in tech can also increase diversity and reduce gender disparity.

Women already in the field should be highlighted through awards, invited to speak at different networking or college events and inspire students through their stories. This will encourage more women to pursue data science. Legislation should ensure that women are paid at par with their male colleagues. Government intervention through policies that protect and promote women in tech can help close this gender gap in the technology sector.


Data science, machine learning, and AI are expected to grow and require more professionals in the future as countries move towards automation and technology. However, these professions must ensure gender equality in the workplace to attract the best talent and encourage innovation.

Differences in salary and monetary rewards, educational qualifications, gender-based discrimination for managerial roles, and a lack of promotional opportunities for women result in a male-dominated tech industry. Traditional stereotypes about women being better suited for healthcare and education also lead to fewer women in data sciences. This gender disparity can be reduced by protecting female employee rights, providing them with better educational opportunities, and encouraging young girls to explore these fields. Specific policies regarding maternity leave, mandatory daycare, and wage laws can also help promote more women in the data science field and reduce this gender disparity.

Disclaimer: Analytics Insight does not provide financial advice or guidance. Also note that the cryptocurrencies mentioned/listed on the website could potentially be scams, i.e. designed to induce you to invest financial resources that may be lost forever and not be recoverable once investments are made. You are responsible for conducting your own research (DYOR) before making any investments. Read more here.

Related Stories

No stories found.
Analytics Insight