Gender Role Challenges

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 keep coming back to this because it exposes the difference between performing gender work and actually doing it. Most programming stalls at the "Sensitive" or "Responsive" levels, accommodating differences without ever challenging the status quo. But recognising a gap is not the same as dismantling the power structure that created it. Here's a quick breakdown of 5 levels of gender integration that has shaped how I think about gender programming 👇 𝐆𝐞𝐧𝐝𝐞𝐫-𝐁𝐥𝐢𝐧𝐝 — The program doesn't consider gender at all. It assumes everyone has the same needs and experiences. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐀𝐰𝐚𝐫𝐞 — You recognise that gender matters, but it stops at acknowledgement. No action follows. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 — You accommodate gender differences. You see them, you plan around them, but you're not yet challenging them. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐯𝐞 — Now we're shifting. The program actively addresses gender inequalities and works to reduce them. 𝐆𝐞𝐧𝐝𝐞𝐫-𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 — This is where we tackle root causes: power, norms, structures, not just symptoms. The jump from Responsive to Transformative is where it gets uncomfortable because it asks us to look at power. At norms. At the structures our programmes often depend on to function. Where does your programme actually sit? That's the question worth sitting with. Save this and share it with your network. #GenderTransformative #GenderEquality #GenderMainstreaming #SystemsChange #SocialImpact

"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."

It's not the pipeline, It's the System. June 23rd is celebrated as 'International Women in Engineering Day" #INWED Sadly the harsh reality, engineering colleges in India produce the highest number of women in STEM graduates/engineers and many of them actually do make it to the workforce. The real challenge is their retention and progression. With 2+ decades in tech and now consulting for tech companies on their Gender Equity Strategy, I’ve seen this challenge firsthand. The issue isn’t talent availability, it’s systemic. In most households, a woman’s career is still seen as optional. That mindset and bias bleeds into workplaces, shaping how women are hired, retained, and promoted. So what can organisations do, 1. Relook at org culture and design. Are your systems, policies, and leadership norms built equitably to support who stays, rises and how. 2. Representation matters, especially in especially in mid and senior levels, invest in retention and have hiring goals across grades. 3. Move from gendered to gender neutral policies. Eg. Maternity to Parental Leave Policy that supports all care-givers. Reframe workplace policies from “women-centric benefits” to equitable caregiving support that normalise shared responsibility and reduce bias. 4. Women in Tech Returnee programs - I've seen immense success in these programs, that offer companies experienced tech talent with a little investment. #Vapasi from Thoughtworks, #Spring from Publicis Sapient are two examples 5. Conduct Stay Interviews, Not Exit Interviews. Understand why women leave and what it takes for them to stay and grow and act on the inputs. 3. A Clear Career Progression Path with mentorship and sponsorship - Bias in growth opportunity for #WIT is real, if there is no intentional support to overcome these bias, talent walks away. 4. I Need to See More Like Me! There is a lack of role models. Accelerated Women in tech leadership programs, fast-tracking the leadership journey of high potential women are some ways to address this. 5. Collective Ownership. Gender Diversity in tech is not a HR, leadership or DEI responsibility. Make it the very fabric of the org. to drive shared accountability. 6. Data is not just diagnostic, it's directional. It guides us on investments to be made, unseen bias and where and what needs to change, it's your mirror don't ignore it. #Inclusion is a organisational capability and leaders are it's torch bearers. Their actions, direction and decisions every single day, signal what truly matters. The Women in tech, talent pool exists. The question is, are you ready to retain, grow, and lead with them? #WomenInTech #WIT #GenderEquity #DiversityInTech Diversity Simplified Image description: A newspaper article titled “It’s Not the Pipeline, It’s the System” from Times of India, Bangalore edition which highlights the gender gap in engineering.

I've watched brilliant gender advisors burn out trying to change their organizations single-handedly. They develop comprehensive gender action plans. Facilitate participatory strategy sessions. Deliver training after training. Check every box. And still, nothing sticks. After 15 years working across international development, NGOs, and corporate settings, I've seen this pattern repeat itself: talented, passionate gender focal points left alone to champion inclusion - while leadership remains unchanged. Here's the uncomfortable truth: Gender equality initiatives fail not because the strategies are wrong, but because leadership hasn't developed the skills to embed them. You cannot delegate organizational transformation to one person or one department. Gender-responsive leadership must be a core competency across all levels - from senior management to team leads. The difference between organizations that achieve real results and those that don't? Their leaders have moved beyond good intentions. They've built concrete skills in: → Gender analysis that informs every decision → Bias interruption as a daily practice → Communication that creates inclusion and psychological safety → Accountability systems that drive change When leadership is gender-responsive and inclusive, those gender action plans don't gather dust. Staff training creates lasting behavior change. Inclusion becomes embedded in how work gets done -- not an add-on managed by one overwhelmed person. This is what happens → Better decision-making. Stronger team performance. Higher retention. Innovation that reflects diverse perspectives. Results that actually reach all stakeholders. This is why I focus my work on building gender-responsive and inclusive leadership capacity throughout organizations - not just supporting gender specialists, but equipping every leader with the skills to drive inclusion. If you're a gender advisor or focal point who feels like you're pushing a boulder uphill alone, this isn't on you. The system needs to change. And if you're in leadership and wondering why your gender and inclusion initiatives aren't delivering results - this is why. What's your experience? Have you seen the difference that gender-responsive and inclusive leadership makes - or witnessed the challenges when it's missing? #GenderResponsiveLeadership #InclusiveLeadership #GenderTrainer #GenderTraining #GenderResponsiveCommunication #GenderResponsiveCommunications

⭐ Today we unveil key insights from our upcoming Outlook Study on #AI and #Gender. Our findings reveal that current AI #policyframeworks often overlook gender considerations. Notably, the Global Index on Responsible AI (GIRAI) indicates that gender equality is one of the lowest-scoring areas in government frameworks. Out of 138 countries assessed, only 24 mention gender in AI, and a mere 18 address it significantly. The study also highlights the specific #risks women face from biased AI systems, such as recruitment #algorithms favoring male candidates. According to MIT’s AI Risk Repository, issues affecting women are predominantly categorized under Discrimination and Toxicity, emphasizing biases that lead to stereotyping and marginalization. Moreover, the lack of gender-disaggregated data hampers our ability to assess the effectiveness of interventions. Our study combines in-depth analysis, real-world examples, and actionable policy recommendations to expose how biases disproportionately affect women, revealing systemic barriers to gender equality in AI. Through the implementation of the Recommendation on the #Ethics of AI and the W4EAI network, UNESCO is committed to driving meaningful change. To foster a more inclusive AI landscape, we must confront these challenges head-on and advocate for diversity and equitable outcomes for all. Let's work together to create a better future!

Gender disability washing does not remove women with disabilities from inclusion by mistake. It removes them to keep inclusion manageable. Gender-disability washing has a pattern. Once you see it, you cannot unsee it. It moves in stages. Every time. 1️⃣ Visible enough to brand. Women with disabilities appear in the photos. In the launch video. In the diversity section of the annual report. The imagery performs inclusion. The infrastructure contradicts it. 2️⃣ Absent from the data. The programme reports gender outcomes. Disability-disaggregated data does not exist. Women with disabilities’ realities dissolve into language about “marginalised groups.” No one is accountable. No one can be counted. 3️⃣ Consulted. Not paid. Not powerful. Women with disabilities are invited to share lived experience at validation workshops after decisions are already made. They are not designing the programme. They are not controlling the budget. Their presence is decorative. Their input is extractive. 4️⃣ First to go when money gets tight. Inclusion was framed as additional never structural. So when budgets shrink, accessibility is the first negotiation. Sign language interpretation becomes not feasible. Transport support becomes not in this phase. The commitment was always conditional. The conditionality was always the point. 5️⃣ Success declared anyway. The report says the initiative reached women in all their diversity. The photos are beautiful. The narrative is compelling. Women with disabilities are absent from every outcome. The washing worked. This is not the exception. This is the norm in far too much gender programming. It persists because it serves a purpose: institutions claim the moral credit of inclusion without bearing the structural cost. Naming it is the first step. Refusing to participate in it is the next. Building systems that make it impossible is the work. #GenderDisabilityWashing #GenderJustice #DisabilityJustice

We've been adding women to projects for 30 years. Development gaps remain. Maybe the problem isn't the women. Maybe it's the projects. Here's what's I think go wrong (at every stage): ↳ At Programming, we conduct gender analysis after the priorities are already set. So the analysis confirms the plan instead of shaping it. ↳ At Identification, we consult women's groups late, after the project concept is fixed. Their input becomes decoration, not design. ↳ At Formulation, we write gender objectives into the logframe but not into the budget. Unfunded objectives aren't objectives. They're intentions. ↳ At Implementation, we count women's participation but not women's power. Attendance is not influence. A room full of women who didn't shape the agenda is not gender mainstreaming. ↳ At Evaluation, we ask "did women benefit?" but not "did the project change anything structural?" So we celebrate outputs and miss the fact that the same barriers will exist in the next project too. The fix isn't complicated. It's just earlier, deeper, and more honest: → Run the gender analysis before political dialogue, not after → Put gender in the Terms of Reference, not just the report → Allocate a real budget line — not a percentage of leftovers → Build M&E indicators that can detect unequal outcomes, not just unequal numbers → Ask at evaluation: what would we design differently next time? Gender mainstreaming isn't a checklist. It's a decision to let evidence about inequality actually change what you do. Thirty years in, that decision is still optional in most project cycles. It shouldn't be. ----- Join my FREE mailing list to receive these insights directly in your inbox 👉 https://lnkd.in/ec8mqV2M

Gender analysis is essential for understanding how social roles, power relations and access to resources shape unequal outcomes for women and men in development and governance processes. The document provides practical guidance on applying gender analysis to planning, programming and organisational practice by clarifying key concepts and illustrating how gender differences can be systematically examined and addressed. This guide developed by the Philippine Commission on Women in collaboration with the Department of the Interior and Local Government brings together the following core elements: – A clear definition of gender analysis as a process for identifying gaps between women and men, understanding why these gaps persist and determining appropriate actions to address them – The rationale for gender analysis, highlighting its role in understanding social processes, identifying gender issues and designing equitable and responsive programmes – Key analytical questions such as who does what, who has access to and control over resources, who decides, who benefits and who loses, used to unpack gender inequalities – Levels of gender analysis, including household and community, project or programme, and organisational or institutional levels, with corresponding tools for each level – The gender analysis planning flow, linking situation analysis, identification of gender issues, strategy development, implementation, monitoring and assessment of results – Practical application of gender analysis in sectoral contexts such as family planning and maternal health, illustrating differential access, roles, responsibilities and impacts – Introduction to specific gender analysis tools, including the Gender Analysis Matrix and the 24-Hour Activity Profile, to examine productive, reproductive, community and leisure roles The document provides an applied and practice-oriented foundation for integrating gender analysis into planning and decision-making, supporting more balanced relations between women and men and more equitable development outcomes.

𝐆𝐞𝐧𝐝𝐞𝐫 𝐦𝐚𝐫𝐤𝐞𝐫 is not simply a task to complete or an obligatory requirement in a proposal package. It should never be treated as a technical formality or a box to tick. Instead, 𝐢𝐭 𝐫𝐞𝐟𝐥𝐞𝐜𝐭𝐬 𝐡𝐨𝐰 𝐝𝐞𝐞𝐩𝐥𝐲 𝐠𝐞𝐧𝐝𝐞𝐫 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭’𝐬 𝐥𝐨𝐠𝐢𝐜 𝐚𝐧𝐝 𝐝𝐞𝐬𝐢𝐠𝐧. It requires a clear understanding of how the intervention is built on solid 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐚𝐧𝐝 𝐠𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐬, and how the project aims to address the 𝐫𝐨𝐨𝐭 𝐜𝐚𝐮𝐬𝐞𝐬 of gender inequalities. A strong gender marker should demonstrate how the project intends to influence and 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐡𝐚𝐫𝐦𝐟𝐮𝐥 𝐠𝐞𝐧𝐝𝐞𝐫 𝐧𝐨𝐫𝐦𝐬 that perpetuate inequality. It should explain how the intervention will contribute to 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐭𝐡𝐞 𝐝𝐚𝐢𝐥𝐲 𝐥𝐢𝐯𝐞𝐬 𝐚𝐧𝐝 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬 of women and girls, not only by including them as beneficiaries but by actively strengthening their position within their families, communities, and institutions. This also means intentionally 𝐞𝐧𝐠𝐚𝐠𝐢𝐧𝐠 𝐦𝐞𝐧 𝐚𝐧𝐝 𝐛𝐨𝐲𝐬 as partners in change, through awareness sessions, dialogue, and community mobilization; so they can play a constructive role in challenging discriminatory practices and influencing others to adopt more equitable behaviors. At the same time, gender-responsive programming must focus on 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐜𝐲 of girls and women. This includes strengthening their awareness of their rights, developing their skills and knowledge, and creating safe spaces where they can express their voices, raise concerns, and influence their peers and communities. Equally important is ensuring that women and girls have improved 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 and meaningful 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 over them, whether those resources are economic opportunities, education, services, or information. Projects should also consider how to 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞, 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐢𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞𝐬, and influence 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬 that shape gender relations and opportunities. Ultimately, the gender marker should capture how the project contributes to transformative change. It is about assessing, holistically, how gender equality is embedded throughout the entire project cycle: from design and planning, to implementation, monitoring, learning, and closure. When gender is genuinely integrated in this way, projects move beyond participation toward transformation. They contribute to shifting power relations, expanding opportunities, and promoting fairness and dignity for everyone. In this sense, a gender marker is not merely a technical score. It is a reflection of the project’s commitment to advancing gender equality and contributing to broader social justice. #IWD2026 #WeObject #GenderEquality #Gender