Diversity In Technology

"Wow, you know your numbers!" "You don’t look like a tech founder!" At some point, I stopped saying “thank you” and started asking, Why is this surprising? Because these aren’t compliments. They’re low expectations wrapped in politeness. As a woman in tech, you learn to spot it early. The way people are impressed when you’re prepared. The surprise when you have clear opinions.The disbelief when you talk systems, not just vision. It’s not flattery. It’s bias disguised as encouragement. I don’t want to be the exception. I want the baseline to shift. So the next time you find yourself impressed that a woman is confident, sharp, and knows what she’s doing — Ask yourself why that still surprises you.

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

I’ve been asked more times than I can count: “So, how did you build Clear without any coding experience?” Plot twist: I can code. 🙄 One quick glance at my LinkedIn profile shows it - software engineering internships, projects, technical skills, and yes, a Physics degree from Imperial College London. And yet, I still hear: 👉 “But you don’t look like someone with a Physics degree.” 👉 “No, but who *actually* built your app?” Here’s the reality: as a founder, I do everything; marketing, sales, business development, HR, fundraising… but also tech and product. In fact, for most of Clear’s journey, the bulk of my day-to-day has been tech and product. So, for the record: almost 5 years into building Clear, I’m still bashing out code and shipping features myself. I’m more technical than most CEOs I know. And yet... I keep having to prove it. Someone even told me recently, “It's because your brand just doesn’t make you seem like a technical founder. If you want people to know you're technical, you need to make it clearer in how you present yourself.” Why is that? Do I need to preface every post with a list of programming languages before it “counts”? Should I show up to my meetings with a printout of my latest git commits? What strikes me is that no one has ever asked our CTO - also a Physics grad from Imperial - if *he* can code. Not once. And I know I’m not alone. I’ve had this same conversation with so many female founders who are technical, but somehow aren’t seen that way unless they constantly parade it. It’s worth reflecting on: if we keep asking technical women “can you actually code?” while assuming men can, we’re not just undermining individuals - we’re reinforcing a narrative that erases technical talent when it doesn’t look the way we expect. So, the next time you find yourself asking a technical female founder whether she "can code", perhaps you should start by asking yourself why you're asking that question in the first place. #femalefounder #entrepreneurship #startups #genderbias #diversity

💻 20 years in IT. That’s how long I’ve worked in this industry. In that time, I’ve built my career on technical knowledge, hard work, and a deep love for what I do. But despite that, I still walk into rooms where people assume I can’t be the technical SME. Not because of my experience. Not because of my skills. But because I’m a woman. Over the years, I’ve had to fight harder to prove myself than male colleagues with less experience. I always believed things would get better, that we’d evolve past those outdated assumptions. Sadly, even in 2025, I still encounter that same disrespect, and not just from men. Sometimes, it comes from other women too. Let’s not forget the women who helped shape this industry, Ada Lovelace, Margaret Hamilton, Dorothy Vaughan. They were pioneers, innovators, and leaders. Women have always belonged in tech. 👉 So here’s my ask: If you work in IT, assume the woman in the room knows her stuff. Assume she’s the SME. Assume she’s a badass. Because more often than not, she is. Let’s break the bias. Together. #WomenInTech #GenderBias #InclusionMatters #TechIndustry #STEM #BiasInTech

"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've never walked into a tech meeting and just been a founder. I've always been a woman founder first. There's a difference, and it costs something. It's rarely one moment you can point to. It's more like a tax: invisible, cumulative, collected before you've said a word. It’s the extra preparation before a technical meeting, not because you lack the knowledge, but because you know you’ll be given less room for uncertainty than the man across from you. It’s revising a sentence multiple times, not for clarity, but to soften how it lands. It’s framing an opinion as a question, not out of doubt, but because confidence in a woman’s voice is often received differently in certain rooms. I've sat in AI and tech conversations where I was the only woman, and watched myself perform a version of competence I don't perform anywhere else. More data. Less instinct. Not a conscious choice. The accumulated result of a thousand small calibrations made over years of reading rooms that weren't designed for you. The real cost isn't personal. It's what never gets said. The instinct edited out before it reached my mouth. The challenge swallowed because I'd already used my allowance for being difficult that meeting. The idea that arrived cautious when it should have arrived sharp. I don't think most men in these rooms intend this. But intention doesn't determine what a room produces. The culture of any room is shaped by what it rewards and what it quietly punishes  and in many tech spaces, certainty reads as competence in a man and aggression in a woman. So this is what I want to leave with the men in those rooms: the sharpest, most original thinking minds might be sitting right across from you slightly hedged, slightly softened, waiting to read whether this is a room where it's safe to be direct. Build that room. It'll make everything around you better.

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.

How Women’s Unique Evaluation Of AI Tools Influences Corporate Culture: “When it comes to adopting AI tools at work, studies have shown that men are more likely to experiment with these tools, while women tend to hesitate. That doesn't mean women are less tech-savvy or less open to innovation. It often means they're asking different questions. And those questions reveal something important about how corporate culture is being shaped in the AI era. Women in the workplace are not saying AI is bad. They’re not rejecting it outright. What they’re doing is pausing. They’re questioning how it works, who created it, what data it was trained on, and whether it could be misused. In many cases, they're also concerned about how others will perceive their use of it. Will they look like they're cutting corners? Will the tool reinforce bias? Will their job become obsolete? That kind of hesitation is discernment and the careful weighing of trade-offs. And it reflects a kind of emotional intelligence and long-term thinking that often gets undervalued in tech conversations. Companies that ignore these perspectives risk designing workflows, cultures, and even ethics policies that leave people behind. If you have a team where the loudest voices are the ones who embrace new tools quickly, and quieter voices are the ones raising concerns, you need to ask yourself: are you hearing the full story? Women may not be the early adopters of every AI tool, but they’re often the first to see unintended consequences. They may be the first to notice that the chatbot is reinforcing stereotypes, or that an AI-powered hiring tool is filtering out qualified candidates based on biased data, which are culture-shaping concerns. I've interviewed hundreds of executives, and the best ones aren't the people who jump on every new technology as soon as it hits the market. They're the ones who ask, ‘Does this make sense for our people? Does it help us do better work? Does it reflect the values we say we care about?’ And more often than not, it’s women who are asking those kinds of questions. Think about what that means in a practical sense. When a company is rolling out a new AI writing tool, a male leader might focus on efficiency. A female leader might ask if the tool risks replacing human insight or if it undermines original thinking. Neither approach is wrong. But they lead to different outcomes.” Read more 👉 https://lnkd.in/enqz6jNy ✍️ Article by Dr. Diane Hamilton #WomenInSTEM #GirlsInSTEM #STEMGems #GiveGirlsRoleModels

Last week, a new Lean In–backed survey found that 78% of men say they use AI at work. 73% of women say they do too. That’s closer than I expected. But that’s not the whole story. Men are more likely to use AI daily or constantly. And that’s where the gap starts to widen. ▶️ Men are more likely to feel confident experimenting with AI and to be recognized for it. ▶️ Women are more likely to approach it cautiously, question accuracy, and think through risks. They are more likely to worry about how their use will be perceived and are more likely to be questioned about it. In other words, employees are not just using AI differently. They are being supported, interpreted, and rewarded differently. AI is quickly becoming embedded in how work gets done and how performance gets evaluated. If recognition doesn’t track with contribution, it shapes who gets trusted, who gets visibility, and ultimately, who advances. New technology doesn’t reset old dynamics. It often reinforces them.

Every day, I speak with multiple businesses across Australia about getting more women into tech... but what about the ones who are leaving? Tech is an industry of innovation, problem-solving, and huge opportunity, yet we’re still losing women every year! From speaking to multiple women each and every week, here’s what I’m hearing; 👉 Many women are still hitting the glass ceiling. They see their peers being promoted over them, despite equal (or greater) experience and impact. Without visibility, sponsorship, and a clear path to leadership, many start questioning if the industry is worth it. 👉 Cultural issues, from microaggressions to being the only woman in the room, many feel unheard and undervalued. 👉 The pressure to overperform just to be seen, combined with outdated workplace policies, is pushing women out. Flexible work, parental leave, and psychological safety aren’t just nice-to-haves; they’re essentials. 👉 Women don’t just need advice; they need decision-makers advocating for them in rooms they’re not in. Without that, climbing the ladder becomes exhausting. I’ve spent over 12 years working in tech recruitment, hosting panels with some of the most brilliant women in the industry, and running Sisterhood Club to create spaces where women can thrive in tech. I know this problem isn’t going away unless leaders step up. That’s exactly why I started: The Big Sister Mentorship Program – connecting women with senior leaders who advocate for them, not just advise them. Career Confidence Podcast – featuring real conversations on career growth, leadership, and what it really takes to succeed. Workshops & Sisterhood Socials – because women need spaces to upskill, connect, and support each other in an industry that still isn’t designed for them. We must acknowledge that Retention is just as important as attraction. Are you ensuring women have clear career growth opportunities? Are your workplace policies built for everyone? Are you actively advocating for and sponsoring women in your teams? Tech needs women. And not just at the entry level; we need them leading, innovating, and shaping the future. What else can be done to keep women in the tech industry? Let me know your thoughts