Harvard Business School: Global Evidence on Gender Gaps and Generative AI
Summary of Read Full Report (PDF)
Examines the gender gap in the adoption and usage of generative AI tools across the globe.Synthesizing data from 18 studies involving over 140,000 individuals, the authors reveal a consistent pattern: women are less likely than men to use generative AI.
This gap persists even when access to these technologies is equalized, suggesting deeper underlying causes. Analysis of internet traffic data and mobile app downloads further supports these findings, indicating a skewed gender distribution among users of popular AI platforms.
The research explores potential mechanisms behind this disparity, such as differences in knowledge, confidence, and perceptions of AI's ethical implications. The authors caution that this gender gap could lead to biased AI systems and exacerbate existing inequalities, emphasizing the need for targeted interventions.
The most prominent explanations behind the gender gap in generative AI adoption are:
- Lower familiarity and knowledge Women consistently report less familiarity with generative AI tools. They are also more likely to report not knowing how to use AI tools.
- Lower confidence and persistence Women show less confidence in their ability to use AI tools effectively. They are also less persistent when using generative AI, being less likely to attempt prompting multiple times for desired results.
- Perception of unethical use Women are more likely to perceive the use of AI in coursework or assignments as unethical or as cheating.
- Mixed perceptions of benefits Studies show mixed results regarding whether men and women equally perceive the benefits and usefulness of generative AI. Some studies indicate women perceive lower productivity benefits and are less likely to see generative AI as useful in job searches or educational settings.
- No significant differences in trust or risk perception The study indicates that gender differences in generative AI adoption are likely driven by disparities in knowledge, familiarity, and confidence, rather than differences in trust or risk perceptions. There are no statistically significant differences in men and women trusting the accuracy of generative AI, or in expressing concerns about risks such as data breaches or job redundancy.