By Amy Do
Generative AI and chatbots equipped with language learning models (LLMs) have advanced exponentially in the past few years. There’s a lot of discourse surrounding their impact on industries across the globe as well as their carbon footprint. Many argued that early adopters would be leaps and bounds ahead of their competitors both in the workplace and the job market, but is that really true? This article will examine the existing literature specifically surrounding generative AI in the workplace through the lens of the gender gap.
A note before we start- this article isn’t as inclusive as it could be. Early data only surveys people who identify themselves as either male or female, and doesn’t disclose information about trans users or early adopters from intersex or genderqueer populations. The data also isn’t as intersectional as it could be- there’s no breakdown of race, age, geological location, or socioeconomic status. It’s going to be interesting to see how all of these factors play out as use of GenAI continues to spread.
I started researching this topic because I initially came across an academic article titled, The Adoption of ChatGPT by Anders Hulum and Emilie Vestergaard from the University of Chicago. They found that half of surveyed workers have used ChatGPT in their workplace, with “younger, less experienced, higher-achieving, and especially male workers leading the curve”. This gender gap in adoption highlighted an unusual trend- because women respond more to information but ‘face barriers that prevent their further adoption’.
The ‘barrier’ that is alluded to in this academic study is the well-documented historic systemic exclusion from women in technology and other STEM fields.
A notable example is the NASA Space program. Originally, President Eisenhower directed NASA to select astronauts exclusively from military test pilots, which were all men. The Defense Department did not allow women to serve in combat aviation positions until 1993- exclusion from the front lines of combat for ‘the fairer sex’.
Sally Ride, along with five other women, became the first female astronauts in NASA’s history in 1978, almost 10 years after the Apollo moon landing. Ride and her colleagues Judy Resnik, Kathryn Sullivan, Anna Fisher, Margaret Rhea Seddon, and Shannon Lucid broke barriers. However, and to this day, the space program was extremely male dominated.
Even now after the all-women Blue Origins “Taking Up Space” flight, only 15% of all astronauts have been women. Men were historically and institutionally supported in their efforts to be scientists, engineers, mathematicians, and physicists, so they did. Women were often discouraged from STEM fields, which were labelled as ‘unladylike’, or a ‘boy’s club’.
This dovetails with current culture of Silicon Valley and generates statistics like gender discrepancies in AI usage. It’s not that women aren’t using it- it’s that in the tech world of early adopters, there aren’t that many women to do the early adopting.
This data can sometimes paint an unrealistic picture that women don’t engage with technology or understand the use cases for GenAI. According to an article in Forbes by Jena McGregor cites a Slack survey that upon first glance shows, “more men are trying AI than women…with 35% of male respondents saying they’ve tried AI for work, compared with 29% of female respondents.”
What isn’t mentioned is that the group surveyed were employees at Workforce Lab is a Salesforce company.
McGregor goes on to state that, “41% of women (surveyed) believe GenAI will hinder their job mobility, compared with 29% of men. It also highlighted the occupations historically performed by women who are most exposed to risks from AI, such as office administrative workers, customer service workers or marketing managers.” Although this caveat is helpful to understand on a larger scale, McGregor doesn’t acknowledge how the original survey data might be skewed due to its pool of respondents. In addition, female dominated fields such as nursing and K-12 education are female-dominated and unlikely to be affected by GenAI.
Marylou Costa provides a deft and accurate insight in a BBC article where she states, “Stem fields [science, technology, engineering, and mathematics] have traditionally been dominated by males…the current trend in the adoption of AI tools appears to mirror this disparity, as the skills required for AI are rooted in Stem disciplines.”
Costa goes on to explain how the makeup of the workforce can directly impact the data:
“In the UK, just 24% of the workforce across the Stem sectors are female, and as a consequence ‘women may feel less confident using AI tools…’ even though many tools don’t require technical proficiency, if more women don’t view themselves as technically skilled, they might not experiment with them.”
Despite the lack of data and industry literacy causing some to declare a moral panic, there are some that counter the narrative.
The Deloitte Center for Technology, Media, and Telecommunications published new predictions in 2024 saying that women’s use of generative AI will “equal or exceed that of men’ by the end of 2025 in the U.S, and within Europe by the end of 2026. A writeup on the report by Radhika Rajkumar, editor of Znet, states, “Women in tech use GenAI for tasks more than men — 44% compared to 33% — and Deloitte found “no notable trust gap between tech women and men.”
It’s not that I don’t think the data is interesting- it is a good starting point to think through all the ways in which women continue to be marginalized and misrepresented. The writers of these reports often fail to acknowledge that this new data is simply a ripple effect, a logical result of historical discrimination and exclusion. Josie Cox in a Bloomberg article titled “Why are Women Less Likely to Use AI?” writes:
“Research shows that women, in many social and professional circumstances, have historically faced graver consequences than men when they’ve failed. That has translated into a resistance to taking risks because women often (correctly) perceive themselves to be at a higher risk of negative consequences.
In the context of using generative AI, failing might mean getting called out for trying to pass a piece of writing off as one’s own, when it was written by a machine — and then being labeled, correctly or not, as unethical or even just lazy.”
The gender gap merely is the result of the lack of women in STEM at a higher level.
Furthermore, the usage of GenAI in the workplace is not a marker of success. ChatGPT and other LLMs are a tool that can be utilized to automate or make easier administrative or ‘busy’ work- work that’s historically been done by women and thus devalued.
Whether usage of GenAI genuinely boosts productivity in a meaningful way long-term is a question that will be surveyed as it’s normalized into onboarding and systemic workflows. I hope that journalists and academics will continue to monitor the impact of biases as are baked into LLM algorithms and generative programs, regardless of industry. Thought leaders should prioritize intersectional testing and surveying practices to gain insights into how different populations are impacted by the adoption of new technologies.
We at the Career Center work to give women and other marginalized groups, along with all DePaul students and alumni, the tools to understand systemic barriers surrounding career literacy and workplace culture. If you’re interested in continuing the conversation or want to discuss further, book a coaching appointment.
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