Sex-disaggregated tech data gaps

"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."

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

My Book Recommendation about the Gender Data Gap: Invisible Women by Caroline Criado Perez 📚 If you're curious about how gender bias is embedded in the systems, technologies, and structures with which we interact daily, I highly recommend reading it. This book is essential reading for anyone working with data, design, technology, policy, or research. It illustrates how the "default male" perspective, which is often unintentional yet deeply ingrained, has real-world consequences for women. Some insights that stayed with me: ➡️ Most crash test dummies are based on average male bodies, which makes women more likely to be injured in car accidents. ➡️ Voice recognition systems are less accurate for women, simply because they have been trained on mostly male voices. ➡️ Urban planning often overlooks unpaid care work, resulting in public transportation systems that are less functional for the people who use them most, who are often women. ➡️ Medical studies overwhelmingly use male participants, which leads to delayed diagnosis and incorrect treatments for women. Perez’s argument is clear: the gender data gap isn’t just a gap; it’s a systemic blind spot. As someone who is currently researching how AI generates visual representations of beauty, this book has sharpened my thinking about who is represented, how, and why. My own research shows that AI image tools such as MidJourney, DALL·E, and Stable Diffusion often depict "beautiful women" as young, white, and hyperfeminine, while offering more variation in their depictions of "beautiful men." Books like Invisible Women help explain why these patterns persist, even in supposedly "neutral" systems. If we want to build more inclusive technologies, we must start by asking better questions and examining the data we use more closely. #genderdatabias #AI #feminism

🚨New series coming up: How to close the Gender Data Gap in Mobility Research🚨 Mobility data influences how we plan our cities, fund innovations, and decide whose daily life counts in transport planning and design. But too often, that data leaves out large parts of the population 😱. I wrote an article and it is not really accessible and maybe even too scientific for non-scientificy people 🤓. That’s why I want to start this series now and summarise the content over the following posts. Each post will focus on one part of the article and follow the process of empirical research from defining questions to interpreting results. They will shed light on where gender biases can sneak in, and how we can do better. It’s not just about the sample composition (but a huge part of course). It’s about how we build knowledge, whose mobility gets seen, and who stays invisible when we try to find out new things. 📚 The article is, unfortunately, really tricky to access via official channels (don’t ask 🙃 I will probably write about the absurd issues with this publishing experience in another post.), but the full preprint is freely available here: 🔗 https://lnkd.in/eDJCJAVj Here is the official link to purchase the article (and others) for 3.42 €  from the archives (!!!🤦♀️!!!)  https://lnkd.in/eqUPFPpV   But here is the gist of it: In transport planning, desing and policy, data is everything. But what happens when the data we use doesn’t reflect the reality of half the population? The Gender Data Gap describes the problem that women* are often underrepresented in mobility data or not represented correctly. It's not just about how many women are included in surveys, but whether we ask the right questions to understand their daily mobility needs. For example, care-related trips, safety concerns, and complex travel chains are part of many women’s everyday mobility but they’re often left out of standard data collection. This leads to biased conclusions and transport solutions that don't work for everyone. This article offers practical recommendations on how to improve mobility data so it better reflects different user realities. It explains why gender matters in everyday mobility, outlines five common biases in data collection, and shows step by step how to design better surveys and analyses. Using examples from the German Mobility Panel (MiD 2017), it illustrates how gender-sensitive data can lead to more accurate, inclusive, and fair transport planning. Whether you work in transport, urban planning, data, or care about equity in mobility: I hope you'll find the insights useful and apply them to make your data more inclusive and valid.🚶♀️🧾🚲   #GenderDataGap #InclusiveMobility #MobilityJustice #TransportEquity #DataMatters #MobilityResearch

Women make up nearly 50% of the population in the U.S., but sadly, only 20-25% of the global AI workforce is female. And when we look at technical roles in healthcare AI? That number drops even lower. If the people building health AI systems don’t represent women; Our health and lived experiences, the outcomes will never fully serve us. Health algorithms are often trained on male-dominated data. And the result is: 👉 Misdiagnosis. 👉 Underperforming tools in women’s health (whether it’s heart disease, pain management, mental health, reproductive care, or menopause research) It gets worse: less than 3% of digital health funding goes toward femtech and women-specific innovation. For women of color, the gaps in data and equity are even starker. This isn’t just a “women’s issue.” It’s a healthcare issue. When we exclude women’s data, we undermine outcomes for half the population. That’s why last month I joined Dr. Ekanjali Dhillon for a powerful conversation hosted by Saltgrass Advisory on Including Women at the Table of Healthcare AI. Ekanjali Dhillon said something that stuck with me: “Women’s health is all of our health.” She’s right. Growing the data means nothing if we don’t grow the voices shaping it. Thank you, Brian Litten, Robert Goodman, and Jacob Jesson for bringing this dialogue forward. The future of AI in healthcare will only be as strong as the diversity of the people building it. Let’s make sure women are not just patients in the system, but architects of it.

This new review in Nature Communications shows how advances in #biomonitoring could help close some of the most persistent evidence gaps in #women’s #healthresearch. Authored by Shaghayegh Moghimi, Lubna Najm, MASc, PMP, Wei Gao, Tohid Didar and colleagues, the paper offers one of the most comprehensive looks at how #biosensing, #wearables, and #digitaldiagnostics can transform women’s health research. For decades, most health technologies have been designed and validated primarily in men. As a result, conditions that affect women—ranging from menstrual and fertility disorders to menopause and chronic diseases—remain understudied and underdiagnosed. This review highlights how new technologies can help close that gap. 💡 ⌚ Wearable and biosensing devices. New generations of sensors are smaller, softer, and better aligned with female physiology. Examples include ovulation-tracking wristbands, sensor-enabled “smart bras” that can detect early breast tissue changes, and noninvasive patches that monitor uterine contractions or fetal health. Some emerging prototypes even track bone density or hormone fluctuations through skin-mounted sensors, allowing for continuous, participant-driven data collection. 🧪Point-of-care and home diagnostics. Portable, low-cost tests using colorimetric or molecular detection (such as loop-mediated isothermal amplification, or LAMP) are expanding access to screening for infections and reproductive conditions. These rapid tests could enable earlier and more equitable diagnosis in both clinical and community settings. Limitations and next steps. The authors note that progress will depend on standardization, validation, and thoughtful integration into healthcare systems. Data quality remains a major barrier. Many devices and algorithms still rely on incomplete or biased datasets that fail to capture the biological and environmental variability across women’s lives. Ensuring that digital health tools are developed with representative, sex-specific data is essential if they are to improve outcomes rather than reproduce existing inequities. Open Access Paper 🔗 https://lnkd.in/dA5GHXua At GSD Health Research, we see this as the central challenge and opportunity for the field. Capturing high-quality, real-world data that reflect the full spectrum of female biology is how we can move from promising prototypes to meaningful clinical impact. #womenshealth #digitalhealth #clinicalresearch