How Gender Blindness Perpetuates Inequality

When gender is not explicitly considered, the result is not #neutrality but the #reproduction of existing inequalities. In our work on gender-sensitive planning in #cities, we consistently confront a critical truth: when urban development fails to incorporate gender mainstreaming and feminist urbanism explicitly, it doesn't simply overlook solutions—it actively #exacerbates structural and spatial violence. #Urbansystems that ignore the differentiated needs of women, girls, and gender-diverse people often reinforce exclusion, insecurity, and inequality. From poorly lit streets and inaccessible public transport to the invisibility of care infrastructure, these design flaws are not coincidental—they are the outcome of planning processes shaped without this very special lens. Our findings repeatedly show that urban spaces designed without a gender perspective perpetuate harm, whether through unsafe environments, unequal access to resources, or invisibility in policymaking. This is not a failure of #policy alone, but of #imagination. We’ve documented hundreds of such cases and approaches—both problematic and transformative. To explore more than 500 articles, tools, and strategies on gender mainstreaming, spatial justice, and inclusive urbanism, visit our website The Gendered City https://genderedcity.org/ Or get the book https://lnkd.in/dt8mCNuy

“I don’t see gender or color.” ❌ It’s a phrase I hear often. The idea is that by “not seeing” identity, we somehow rise above bias. But the truth is, this mindset doesn’t eliminate discrimination. It disguises it. When we say we don’t see gender, we erase the reality that women are consistently paid less for the same work, are underrepresented in leadership, and carry the greatest burdens in war and conflicts. ❤️🩹 When we say we don’t see color, we ignore the fact that communities of color are more likely to be denied housing, financial assistance, or equitable access to aid. Choosing not to see these realities doesn’t make them disappear. It makes them easier to excuse. “Colorblindness” and “gender-blindness” allow inequity to continue unchallenged, because if we don’t acknowledge the differences, we don’t have to acknowledge the discrimination either. Seeing gender and race clearly means recognizing how systems are stacked, whose voices are left out, and whose opportunities are blocked. The goal is not blindness. The goal is vision. To look honestly at how inequality is built into our structures, and to act deliberately to dismantle it. ✨

Invisible Women: The Cost of Designing a World for Men Just finished reading Invisible Women by Caroline Criado Perez —and I can't stop thinking about it. This book is a must-read for anyone in data, policy, product design, healthcare, urban planning... really, anyone who makes decisions that impact people. The central message? Our world is built on data that defaults to men—and it’s failing women as a result. From the design of seat belts and smartphones, to the layout of cities, drug trials, and workplace policies, Invisible Women exposes how gender-blind data collection leads to gender-biased outcomes. One insight that hit me hard: "Gender-neutral" often just means male by default. For those of us working in Data & Analytics, it’s a compelling reminder that what we choose to measure—or ignore—has real-world consequences. We have a responsibility to challenge defaults, question assumptions, and ensure inclusion is embedded in the way we design, collect, and act on data. No data should be considered objective if it excludes half the population. Especially in the age of AI. Have you read it? Would love to hear your thoughts. #DataBias #InclusiveDesign #WomenInData #Leadership #InvisibleWomen #CarolineCriadoPerez #Equity #DiversityInTech #DataForGood

AI systems built without women's voices miss half the world and actively distort reality for everyone. On International Women's Day - and every day - this truth demands our attention. After more than two decades working at the intersection of technological innovation and human rights, I've observed a consistent pattern: systems designed without inclusive input inevitably encode the inequalities of the world we have today, incorporating biases in data, algorithms, and even policy. Building technology that works requires our shared participation as the foundation of effective innovation. The data is sobering: women represent only 30% of the AI workforce and a mere 12% of AI research and development positions according to UNESCO's Gender and AI Outlook. This absence shapes the technology itself. And a UNESCO study on Large Language Models (LLMs) found persistent gender biases - where female names were disproportionately linked to domestic roles, while male names were associated with leadership and executive careers. UNESCO's @women4EthicalAI initiative, led by the visionary and inspiring Gabriela Ramos and Dr. Alessandra Sala, is fighting this pattern by developing frameworks for non-discriminatory AI and pushing for gender equity in technology leadership. Their work extends the UNESCO Recommendation on the Ethics of AI, a powerful global standard centering human rights in AI governance. Today's decision is whether AI will transform our world into one that replicates today's inequities or helps us build something better. Examine your AI teams and processes today. Where are the gaps in representation affecting your outcomes? Document these blind spots, set measurable inclusion targets, and build accountability systems that outlast good intentions. The technology we create reflects who creates it - and gives us a path to a better world. #InternationalWomensDay #AI #GenderBias #EthicalAI #WomenInAI #UNESCO #ArtificialIntelligence The Patrick J. McGovern Foundation Mariagrazia Squicciarini Miriam Vogel Vivian Schiller Karen Gill Mary Rodriguez, MBA Erika Quada Mathilde Barge Gwen Hotaling Yolanda Botti-Lodovico

Prof. Claudia Goldin made history by becoming the first woman to win a solo Nobel Prize in Economics. She was recognized for her ground-breaking work on the key drivers behind gender differences in the labour market. Goldin’s work shows that although historical factors such as disparities in education have narrowed in modern times, the earnings gap between men and women remains.   One reason for this is a lack of opportunities. This is where behavioural science may be able to help by identifying implicit biases and engineering a choice architecture to help tackle them.   Acknowledging the difficulty in proving that discrimination on the basis of gender in the workforce exists, Goldin and her colleagues at Harvard University turned their attention to one occupation which attempted to combat gender-biased hiring – musicians.   Before 1980, none of the “Big 5” symphony orchestras in the U.S. contained more than 12% female musicians due to both implicit and explicit biases in the hiring process. To combat these, orchestras began implementing “blind” auditions – that is, the candidate performed behind a screen so that the committee could not identify them as male or female.   This helped a little, but less than expected until they added a carpet.   What?   It turned out that the committee could hear the click of women’s shoes as they walked on stage and even that was sufficient to bias their decisions!   Analyzing data from 11 orchestras who implemented these changes revealed some shocking statistics. By hiding the identity of the musician in the audition, there was a 50% increased likelihood of a female musician progressing to the next round of auditions.   Goldin further estimated that blind auditions accounted for about 25% of the increase in the number of female orchestra musicians from 1970 to 1996. (Other factors like training more female musicians also contributed to this growth).   So, what does this teach us? 👉 Being blind to the #gender (as well as to other things like race) can improve impartiality in #hiring 👉 Biases are persistent and creep into decision making through the smallest of gaps (e.g. no carpet!) 👉 A carefully designed choice architecture can help to mitigate hiring biases and enhance #equity in the workplace Do you know of other innovative ways organisations are changing hiring processes to be as unbiased as possible?   #DiversityAndInclusion

This morning, I was reading about Susan Bennett, an American voice actress. You probably don’t know that you know her—she’s the original voice of Siri 💡 That got me wondering: why do most virtual assistants -like Siri, Alexa, and Google Assistant- default to female voices? If you think it’s just a coincidence, I urge you to think again. For decades, society has associated women with caregiving, support roles, and emotional labor. When we program AI assistants to use female voices, we “unintentionally” reinforce the stereotype that women exist to serve, assist, and make life easier for others. Research shows that users perceive female voices as "calmer" and "more soothing". Qualities we’ve been socially conditioned to expect from women. But there’s a darker side to this design choice: it perpetuates gender bias, normalizing the idea of women as subordinates, always available to meet others' needs. By assigning female voices to assistant roles, we are embedding outdated biases into cutting-edge technology & into our future. And then we wonder why it’s still going to take 139 years to close the gender gap. This is a small yet profound example of how unconscious bias seeps into the systems we create and why inclusion in tech is so crucial. How can we ensure that technology challenges stereotypes instead of reinforcing them? And why not building a future where innovation serves equity, not just convenience. #BiasInTech #AIAndSociety #GenderEquality #InclusiveInnovation #FutureOfTech #TechForGood #BreakTheBias #DiversityInAI #WomenInTech #EquityInInnovation

Guess what? Only 30% of AI professionals are women! According to the Global Gender Gap Report 2023, this imbalance fuels gender biases in AI, making it inherently sexist. Yes, you heard it right. The world has a gender equality problem, and so does Artificial Intelligence (AI). While we are making great strides in gender equity across various fields and more women are accessing the internet daily, the truth is that women still do not get to create much of this technology. Many women use AI, but few get to build it. Does that matter? The truth is, it does. It's like using a half-baked cake every day. A study by the Berkeley Haas Center for Equity, Gender, and Leadership analyzed 133 AI systems across different industries and found that about: 💻 44% showed gender bias 💻 25% exhibited both gender and racial bias This might not sound real, but it is. The systems that change our lives every day are still primarily based on the lives of men, not women. AI is mostly developed by men and trained on datasets that are primarily based on men, leading to responses that are not inclusive, inaccurate, and unrealistic for women. When technology is developed with just one perspective, it’s like looking at the world half-blind, and that can never ensure a gender-equitable world. The question now is, how can we ensure that technology is gender equitable? To prevent gender bias in AI, we must first address gender bias in our society: 📍Increase Women's Participation: Encourage more women to not only use but also create technology. We need more women researchers in AI. The unique experiences of women can profoundly shape the foundations of technology, paving the way for new and inclusive applications. 📍Draw on Diverse Expertise: Integrate diverse fields of expertise, including gender expertise, when developing AI. This ensures that machine learning systems serve everyone better and support the drive for a more equal and sustainable world. 📍Promote Inclusive Data and Decision-Making: In a rapidly advancing AI industry, the lack of gender perspectives, data, and decision-making can perpetuate inequality. For a gender-equitable world, we need gender-equitable technology and AI. In summary, for AI to be truly transformative and inclusive, we need a diverse and gender-balanced workforce. Let’s work towards a future where technology serves everyone equally. LinkedIn Guide to Creating LinkedIn for Nonprofits LinkedIn News India #GenderEquity #WomenInAI #BiasInTech #InclusiveTech #SustainableFuture

I wrote a blog post with Foundation for Alcohol Research and Education (FARE) on the dangers of a gender-blind approach to alcohol control. While gendered violence prevention agencies in Australia have acknowledged that there are social and cultural norms linking alcohol and male violence, they have neglected other relevant factors, including: acceptability (including how alcohol is marketed), availability (including how alcohol is regulated) and affordability (including unit pricing). Efforts to change gender norms are swimming upstream against harmful industries who profit from violence against women. Their marketing and lobbying budgets dwarf public investment in campaigns for gender equality and respectful relationships. If we aren't prepared to confront vested interests, then we won't prevent gender based violence. https://lnkd.in/gV5kPXjU