Systemic exclusion of women in tech training

As GenAI becomes more ubiquitous, research alarmingly shows that women are using these tools at lower rates than men across nearly all regions, sectors, and occupations.   A recent paper from researchers at Harvard Business School, Berkeley, and Stanford synthesizes data from 18 studies covering more than 140k individuals worldwide.   Their findings:   • Women are approximately 22% less likely than men to use GenAI tools • Even when controlling for occupation, age, field of study, and location, the gender gap remains • Web traffic analysis shows women represent only 42% of ChatGPT users and 31% of Claude users   Factors Contributing the to Gap:   - Lack of AI Literacy: Multiple studies showed women reporting significantly lower familiarity with and knowledge about generative AI tools as the largest gender gap driver. - Lack of Training & Confidence: Women have lower confidence in their ability to effectively use AI tools and more likely to report needing training before they can benefit from generative AI.   - Ethical Concerns & Fears of Judgement: Women are more likely to perceive AI usage as unethical or equivalent to cheating, particularly in educational or assignment contexts. They’re also more concerned about being judged unfairly for using these tools.   The Potential Impacts: - Widening Pay & Opportunity Gap: Considerably lower AI adoption by women creates further risk of them falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities. - Self-Reinforcing Bias: AI systems trained primarily on male-generated data may evolve to serve women's needs poorly, creating a feedback loop that widens existing gender disparities in technology development and adoption.   As educators and AI literacy advocates, we face an urgent responsibility to close this gap and simply improving access is not enough. We need targeted AI literacy training programs, organizations committed to developing more ethical GenAI, and safe and supportive communities like our Women in AI + Education to help bridge this expanding digital divide.   Link to the full study in the comments. And a link also to learn more or join our Women in AI + Education Community. AI for Education #Equity #GenAI #Ailiteracy #womeninAI

"This report developed by UNESCO and in collaboration with the Women for Ethical AI (W4EAI) platform, is based on and inspired by the gender chapter of UNESCO’s Recommendation on the Ethics of Artificial Intelligence. This concrete commitment, adopted by 194 Member States, is the first and only recommendation to incorporate provisions to advance gender equality within the AI ecosystem. The primary motivation for this study lies in the realization that, despite progress in technology and AI, women remain significantly underrepresented in its development and leadership, particularly in the field of AI. For instance, currently, women reportedly make up only 29% of researchers in the field of science and development (R&D),1 while this drops to 12% in specific AI research positions.2 Additionally, only 16% of the faculty in universities conducting AI research are women, reflecting a significant lack of diversity in academic and research spaces.3 Moreover, only 30% of professionals in the AI sector are women,4 and the gender gap increases further in leadership roles, with only 18% of in C-Suite positions at AI startups being held by women.5 Another crucial finding of the study is the lack of inclusion of gender perspectives in regulatory frameworks and AI-related policies. Of the 138 countries assessed by the Global Index for Responsible AI, only 24 have frameworks that mention gender aspects, and of these, only 18 make any significant reference to gender issues in relation to AI. Even in these cases, mentions of gender equality are often superficial and do not include concrete plans or resources to address existing inequalities. The study also reveals a concerning lack of genderdisaggregated data in the fields of technology and AI, which hinders accurate measurement of progress and persistent inequalities. It highlights that in many countries, statistics on female participation are based on general STEM or ICT data, which may mask broader disparities in specific fields like AI. For example, there is a reported 44% gender gap in software development roles,6 in contrast to a 15% gap in general ICT professions.7 Furthermore, the report identifies significant risks for women due to bias in, and misuse of, AI systems. Recruitment algorithms, for instance, have shown a tendency to favor male candidates. Additionally, voice and facial recognition systems perform poorly when dealing with female voices and faces, increasing the risk of exclusion and discrimination in accessing services and technologies. Women are also disproportionately likely to be the victims of AI-enabled online harassment. The document also highlights the intersectionality of these issues, pointing out that women with additional marginalized identities (such as race, sexual orientation, socioeconomic status, or disability) face even greater barriers to accessing and participating in the AI field."

I keep hearing studies about how women are behind in AI, and I can’t help but wonder if this is just sewing a biased seed. Like a self-fulfilling prophecy, tell women they’re behind and they will question whether they should start or continue going. We’re seeing the same narrative pattern as when we talk about women in cybersecurity—where women were the OG computers, programmers, cryptologists until men saw dollar signs and drove them out. Women have played pivotal founding roles in AI and, yet now we’re trying to convince them they’re late to the party because there’s money being made. If women are behind in AI, it’s because men are hogging the time to learn through disproportionate distribution of the mental load in families and organizations. Studies have proven that men have more leisure time than women, giving them an advantage of capacity to read, learn, and participate in conversations about AI. Bottom line is we need diverse perspectives building and interacting with AI to ensure mitigation of biased implementation and outputs. In order to make AI participation more inclusive, we need to address the systems and societal norms that are contributing to the imbalance of opportunity and signals of discouragement. Companies can achieve this through… … caregiver support … flexible work schedules … providing time on the clock for study … encouraging men to take leave for family … approve professional development requests for AI training equitably And also pay equitable wages to ensure women have equitable financial opportunity to pay for training out of pocket if that’s necessary. Let’s stop talking about women being behind in AI, and keep the conversation on actionable, equitable access and inclusion for everyone.

My recent research, which examines the adoption of emerging technologies through a gender lens, illuminates continued disparities in women's experiences with Generative AI. Day after day we continue to hear about the ways GenAI will change how we work, the types of jobs that will be needed, and how it will enhance our productivity, but are these benefits equally accessible to everyone? My research suggests otherwise, particularly for women. 🕰️ The Time Crunch: Women, especially those juggling careers with care responsibilities, are facing a significant time deficit. Across the globe women spend up to twice as much time as men on care and household duties, resulting in women not having the luxury of time to upskill in GenAI technologies. This "second shift" at home is increasing an already wide divide. 💻 Tech Access Gap: Beyond time constraints, many women face limited access to the necessary technology to engage with GenAI effectively. This isn't just about owning a computer - it's about having consistent, uninterrupted access to high-speed internet and up-to-date hardware capable of running advanced AI tools. According to the GSMA, women in low- and middle-income countries are 20% less likely than men to own a smartphone and 49% less likely to use mobile internet. 🚀 Career Advancement Hurdles: The combination of time poverty and tech access limitations is creating a perfect storm. As GenAI skills become increasingly expected in the workplace, women risk falling further behind in career advancement opportunities and pay. This is especially an issue in tech-related fields and leadership positions. Women account for only about 25% of engineers working in AI, and less than 20% of speakers at AI conferences are women. 🔍 Applying a Gender Lens: By viewing this issue through a gender lens, we can see that the rapid advancement of GenAI threatens to exacerbate existing inequalities. It's not enough to create powerful AI tools; we must ensure equitable access and opportunity to leverage these tools. 📈 Moving Forward: To address this growing divide, we need targeted interventions: Flexible, asynchronous training programs that accommodate varied schedules Initiatives to improve tech access in underserved communities. Workplace policies that recognize and support employees with caregiving responsibilities. Mentorship programs specifically designed to support women in acquiring GenAI skills. There is great potential with GenAI, but also risk of leaving half our workforce behind. It's time for tech companies, employers, and policymakers to recognize and address these gender-specific barriers. Please share initiatives or ideas you have for making GenAI more inclusive and accessible for everyone. #GenderEquity #GenAI #WomenInTech #InclusiveAI #WorkplaceEquality

I’ve spent a decent amount of my career in tech and have worked alongside many incredibly capable women. Despite our talent, too many of us had to fight much harder than their male counterparts because of: ✔ Less sponsorship ✔ Lower visibility and slower promotion rates ✔ Greater scrutiny when taking risks Now I think about these women even more. I have a daughter who will likely be entering the workforce in the next 10 years. If AI fluency is becoming the new career currency, what does this moment mean for her? New data makes this question hard to ignore: - According to the Women in the Workplace report from McKinsey & Company & LeanIn.Org, entry-level women are significantly less likely than entry-level men to be encouraged by managers to use AI tools (21% vs. 33%) and far fewer believe AI will help their careers. - Broader studies show this isn’t isolated: women are using AI at lower rates than men even after controlling for job, age, and education, and are less certain that AI tools will advance their careers. Forbes - A global talent analysis from Randstad finds a 42% gap in workers who identify as AI-skilled: 71% of those are men, only 29% are women. And women receive fewer AI upskilling opportunities and less confidence that the training they get will matter. Meanwhile, LinkedIn and labor market data show women are overrepresented in jobs most likely to be disrupted by AI and underrepresented in “augmented” roles that benefit from AI tools. All of which compounds into what feels like a new digital glass ceiling -one built not just by structural bias, but by unequal access to and encouragement in AI learning and adoption. Call to action: Schools & universities: Treat AI literacy like basic career readiness. Don’t make it elective or self-selecting. CEOs & CHROs: Close the AI encouragement gap now. Make AI access, experimentation, and sponsorship standard for early-career talent - not optional and not manager-dependent. This generation of women is ambitious like all the prior generations before it. The question is whether the system will finally meet them halfway...or...quietly fall behind them again. The future of work shouldn’t be a replay of the past. #WomenInTech #AI #FutureOfWork #Leadership Pamela Norton, Marenza Altieri-Douglas, Ginniee Sahi, Nour Al Hassan, Rina Charles, Mini Suri

A lot of women are taught to admire tech from a distance, not enter it. Not explicitly. But subtly, repeatedly, over time. You grow up seeing tech treated like a male domain. A world of coders, engineers, jargon, and certainty. And if you are a woman from a non-technical background, it can feel even further away. You start to believe tech is for people who have been building since they were 15, for people with the right degrees. For people who sound technical the moment they open their mouth. And many women do what we have been conditioned to do in rooms like that: we underestimate ourselves before we even begin. I know that feeling. I came from media. From storytelling, content, audiences, emotion, communication. Not from engineering labs. Not from computer science. Not from deep technical training. And today, I work on speech infrastructure. If you had told me earlier that my work would one day involve thinking deeply about evaluation systems, model behaviour, infrastructure, and how AI systems perform in the real world, I would probably have laughed. Not because I could not do it. But because I had quietly absorbed the idea that this world was not really meant for me. I think that is true for many women. Our fear of tech is often not about ability. It is about conditioning. It is about being made to feel that technical confidence belongs to someone else. Some of the smartest women stay away from tech not because they lack the capacity, but because they think they need permission they were never given. This is why I think this moment is so important. AI is changing the entry point. It is reducing the distance between “I don’t understand this” and “let me explore this.” It does not replace deep expertise but it does make the road into tech less intimidating. And that is huge for women. My own move from media into speech infra has shown me this very clearly: you do not need to begin as a technical person to become one. You need curiosity. You need courage. You need the willingness to stay in the discomfort of not knowing. So to every woman who has felt that tech is intimidating, alien, or somehow not for her: Please do not mistake that fear for truth. A lot of that fear was taught. A lot of it came from culture, not capability. And a lot of it can now be unlearned. Give tech a chance. Not because you need to become someone else. But because this is one of the first moments where the walls around it are actually starting to come down. You do not have to fear technology to build with it and you definitely do not need a traditional technical background to belong in the room. Sometimes, all that changes a life is realizing the room was yours to enter all along.

Your AI recruitment tool is probably biased. The real problem? Your tech team might not have the skills to spot it. Or does not know how to fix it. I've been deep in dissertation mode, processing almost 2,000 papers for my literature review on AI, gender equity, and HR, with a side topic of Human-AI collaboration(yes, I counted). Most of the papers are theory built on theory built on theory. Important work, but not exactly helping leaders figure out what to do Monday morning. We're drowning in frameworks while discrimination gets coded into systems every day. Zhou et al.'s (2023) research stands out refreshingly from the usual suspects. While most researchers are building increasingly complex mathematical frameworks to measure bias (here's your fish) Zhou's team asked a different question: What if we taught people to recognize bias themselves? (here's how to fish). Instead of another framework, they built hands-on tutorials where AI developers actually manipulate biased datasets and watch AI predictions change in real-time. It's just a python code away: You're tweaking a recruitment algorithm's training data. Add more male engineers from 2010. Watch the AI suddenly score women's resumes lower. Remove those data points. Watch scores equalize. That's bias becoming code in real-time. The results? 18 participants (small study, big implications) showed measurable improvements in both recognizing AND addressing bias after these tutorials. Not from lectures. Not from frameworks. From experiencing it themselves. Why this matters for leaders: 1-Your tech teams might not recognize bias when it's happening 2-Your HR teams might not understand how it gets encoded 3-Traditional AI and CS training isn't bridging this gap. Yet. The elegantly simple insight: Let people see discrimination becoming code. Watch understanding follow. For those wrestling with bias in recruitment or performance systems: what would change if your teams could actually see how these biases take root? Sometimes the path forward isn't another policy document. It's helping people truly understand what they're building. What's been your experience? While we theorize, how many biased decisions are our systems making right now? #AItraining #GenderBias #HRTech #AIinHR #ResponsibleAI #FutureOfWork

Just read the Lovelace Report 2025 and the numbers have genuinely shocked me 🤯 The UK tech industry is losing £2–3.5 billion every year because women are leaving roles or leaving tech altogether. And it’s not down to lack of skill or ambition. It’s down to the environments we’re putting them in - lack of progression, unfair pay, poor recognition, cultures that don’t make people want to stay. This is an urgent need for change. Because every time we lose someone from underrepresented backgrounds, we lose not just talent - we lose perspective, creativity, ideas that never got to show up. A few things I think help companies actually turn this around: 💜 Transparent career paths - people need to see where they can go, not just what they’re doing now. 💜 Recognition and reward - fairness in pay + visibility when people deliver. 💜 Culture that listens - safe feedback, sponsorship, mentorship. If we want data & AI to solve meaningful problems, we need the teams building them to truly represent the world they impact. This is exactly why we started she does data. - to inspire more women into data & tech, and to make sure they’re not just entering the industry, but staying, growing, and leading in it 💜💪 If diversity is worth billions to the UK economy… why are we still treating it like an afterthought? #diversityindata #DEI #shedoesdata #femalesindata #femalesintech Oliver Wyman WeAreTechWomen