Lean In’s survey shows how women are losing out in the AI age. Learn how women and men compare in AI adoption in the workplace, recognition, and more.
The AI gender gap | Datawrapper Blog
In this Weekly Chart, we examine the gender gap in AI adoption.
Opinion: A gender gap is emerging in AI adoption, and women risk being left behind
Women appear less inclined to use AI tools, producing a disadvantage they can ill afford
The Gender Gap In AI Use—And What’s Driving It, According To Lean In Survey
The new data from Lean In reveal a gender gap in AI use, as well as in support and recognition for those who do use it.
AI adoption higher among men than women: Survey
This large gap — 81.3% compared to 58.8% — stems from the higher representation of women in skilled trades, where AI usage remains extremely low.
Women executives warn AI adoption is outpacing workforce development
A Chief and Harris Poll survey reveals how women leaders are shaping AI adoption by prioritizing workforce development and ethical implementation over speed.
The 'AI gender gap' narrative is missing the full picture | Fortune
New research shows women leaders are shaping AI strategy, ethics, and oversight—even as concerns about adoption gaps persist.
Report: Where are the women? Mapping the gender job gap in AI
As AI becomes ubiquitous in everyday life, closing the gender gap in the AI and data science workforce matters.
The Large Gender Gap in Who Uses AI
A recent study finds that AI usage tilts heavily toward men. Part of the reason: Women worried they might be penalized for using AI.
Sheryl Sandberg: The AI gender gap is about recognition
New Lean In survey says bosses aren't giving women credit for using AI.
The Beauty of AI and Why AI Needs More Women
While women remain underrepresented in the Artificial Intelligence (AI) and tech workforce, their participation is considered crucial for developing the right AI for tomorrow.
Don't Let Women Get Left Behind: Why the Future of AI Upskilling Must Be Inclusive
Women still lag in AI adoption at work. The risk is we'll leave talented women behind and hurt business performance. Tips to close the gap.
A Proposed Conceptual Framework for Understanding Gender Disparities in Artificial Intelligence Adoption
This conceptual paper investigates the issue of gender disparities in artificial intelligence adoption within organizational settings. It highlights the importance of addressing these differences for workplace inclusivity and performance. The problem lies in the unequal of artificial intelligence adoption between male and female employees’ perspectives. The proposed framework is developed through a review of existing literature highlights differences between male and female employees in their artificial intelligence adoption. The methodology is based on conceptual analysis, derive on previous studies to build a comprehensive understanding of gender disparities in artificial intelligence adoption. An Independent T-Test will be used to analyze the disparities between male and female Malaysian sports graduates in testing the hypothesis development. By addressing these disparities, the paper provides insights into creating inclusive strategies that promote gender equity in the workforce. Overall, the findings aim to guide both researchers and practitioners in designing gender equality policies that address challenges to equal adoption of artificial intelligence.
Women in AI: Numbers Behind the Gender Gap in Tech
How is AI changing the game for women in 2026? From job shifts and personal life-hacks to the rise of female AI founders, explore the unique impact of women in tech today.
New research reveals "staggering" gender gap in ChatGPT adoption
A study reveals that ChatGPT adoption reflects existing workplace inequalities, with younger, higher-paid men using AI more than women and lower-income employees.
Paid Program: Why More Women Are Needed in AI
When the world of AI isn’t inclusive, it creates biases in what the data shows and how it’s used.
Researchers uncover AI bias against older working women
AI is perpetuating inaccurate gender and age stereotypes, influencing everything from hiring practices to workplace perceptions.
