Gender Divide on AI Adoption

Are men more likely than women to adopt generative artificial intelligence tools in their work?  Indeed, that is what scholars Nicholas Otis, Solène Delecourt, Katelynn Cranney, and Rembrand Koning have discovered. They recently published a working paper titled "Global Evidence on Gender Gaps and Generative AI."  Here is an excerpt from their paper:

The findings above document that gender gaps in generative AI are nearly universal. We find women use generative AI less than men in data from 18 studies covering 143,008 people from across the world as well as data on who uses top generative AI websites and apps. Moreover, equalizing access does not appear to fully close the gap, even when presented with the chance to use generative AI, women are less likely to use this new technology than men. Akin to efforts to equalize female labor market participation and pay (England, Levine, and Mishel, 2020), it appears that social, cultural, and institutional frictions have led to a gendered gap in generative AI adoption.

Why does this gender gap exist?  HBS Working Knowledge recently interviewed Rembrand Koning about the research.  The article, by Michael Blanding, reports that, "the research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them."  

Why is the gender gap finding so consequential?  Two important ramifications must be considered.  First, will the differential usage lead to the exacerbation of gender biases?  In their paper, the scholars write:

This disparity has the potential to be significant. As generative AI systems are still in their formative stages, the under-representation of women may result in early biases in the user data these tools learn from, resulting in self-reinforcing gender disparities (Cao, Koning, and Nanda, 2023). Such biases in user data—similar to those that have previously led to racial disparities in generative AI performance—could result in generative AI systems that reinforce gendered stereotypes and in tools that are less effective at the tasks more often performed by women (Koenecke et al., 2020; Guilbeault et al., 2024).

Secondly, the lower adoption rates by women may affect their career opportunities and future compensation, according to these scholars.  Thus, further work will be needed to understand what's driving the gender gap, and how it may be affecting employees and their careers.