Social Impact Of AI

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

In rural India, women are tasked with watching sexual and violent content every day to train AI, bearing a heavy emotional toll. They label hundreds of images and videos under strict productivity pressures, often in home-based roles that can be exploitative. India’s $250 million data-labeling industry profits while most of the financial and emotional costs remain with these workers. Behind every “safe” digital feed are human lives silently enduring trauma. This story is based on the detailed reporting by Anuj Behal in The Guardian. You can read the article here: https://lnkd.in/g-zX3kgD

"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."

The AI Gender Gap Is Not Inevitable. Earlier this week I wrote about AI reshaping careers and firms tying bonuses to AI usage. There was one dimension I did not talk about. It is about women. Men are 22 per cent more likely than women to use AI daily at work. When women do use it, 18 per cent have been praised by their managers this is compared with 27 per cent of men. Half of women surveyed said using AI at work “feels like cheating.” Only 43 per cent of men agreed. (Lean In, April 2026) A Harvard meta-analysis covering 143,000 people across 25 countries found women had 22 per cent lower odds of using generative AI. The gap widens with age. And the number that really impacts this conversation? Women make up 57 per cent of US workers in roles likely to be disrupted by AI. (ILO / WEF) Women are not just less likely to be using AI. They are more likely to be in the jobs AI replaces. This feels like a management problem, not a pipeline problem. Encouragement is unequal, managers are more likely to push men to experiment. Recognition is biased, emerging research suggests women get less credit for the same AI usage. And time is unequal, when upskilling happens in people’s own time, it excludes those carrying three times the unpaid care work by looking after children, parents or the home. But here is the half of the story that gets lost. Female AI talent has expanded significantly since 2018. The gender gap in AI skills has narrowed in 74 of 75 economies. The overwhelming majority of women in tech would move into AI-focused roles with the right support. When Microsoft backed a free AI programme for women, 57,000 signed up across 30 countries that is a 570 per cent above target. In the London Market, the skills that matter most, structuring complex programmes, maintaining client relationships through difficult claims, exercising judgment when data is incomplete these are the skills AI cannot replicate. Those skills are not gendered. But access to the tools that will sit alongside them increasingly is. So how do we change the situation? Measure AI adoption by gender. Fund training during working hours. Audit whether your incentive structures reward women at the same rate as men. And make this a leadership conversation, not only an HR conversation. Women in this market negotiate complex international programmes. They structure reinsurance treaties. They make decisions under pressure that no algorithm can replicate. Give them the tools. Give them the time. And then stand back and watch them succeed.

AI is helping mothers in Kenya access the care and information they need, right when it matters most. In too many communities, pregnancy and early motherhood happen without the support or guidance that every woman deserves. Jacaranda Health is changing that through PROMPTS, an AI-powered SMS platform that delivers timely, accurate health information in Swahili. The impact is clear: → Women are 20% more likely to attend recommended prenatal visits → Nearly twice as likely to use postpartum family planning services → 18% more likely to return for critical postpartum care PROMPTS now responds to over 10,000 messages a day—70% handled instantly through AI, with urgent questions directed to trained nurses.  And with new weather data integrations, it’s also helping protect maternal health during extreme heat and environmental stress. Read the full grantee story, link in comments.

𝗪𝗼𝗺𝗲𝗻 𝗮𝗿𝗲 𝗻𝗲𝗮𝗿𝗹𝘆 𝟯𝗫 𝗺𝗼𝗿𝗲 𝗹𝗶𝗸𝗲𝗹𝘆 𝘁𝗼 𝗹𝗼𝘀𝗲 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯𝘀. As per a recent report published by the UN, in high-income countries, women are 3X more likely than men to hold jobs at high risk of automation. Worldwide, in formal workplaces more women than men hold jobs like admin assistants, secretaries, bank tellers, data entry staff, customer service. For millions of women, these are not job roles, these are their lifelines. They provide women- - their first step into the formal economy.  - a source of financial independence. - a stable income that keeps families afloat. - and a dignified path to self-worth. When these roles vanish, women are going to lose access to job market, they will lose their agency and would run a risk of losing their security. When we talk about AI coming for your job, it's mostly women who are going to be swept by automation. Agreed, AI will boost productivity. That's the promise. But as Ray Dalio warns, we may be entering a “great deleveraging” where tech outpaces our ability to transition people fast enough. Especially for women. That threat from AI runs deeper than this. First, 𝘄𝗼𝗺𝗲𝗻 𝗮𝗿𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘃𝗲𝗿𝘆 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗔𝗜 𝗶𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴. Women are underrepresented in STEM, data science and AI development a such. 𝗪𝗵𝗲𝗻 𝘄𝗼𝗺𝗲𝗻 𝗮𝗿𝗲 𝘂𝗻𝗱𝗲𝗿𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗲𝗱 𝗶𝗻 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, 𝘁𝗵𝗲𝗶𝗿 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀, 𝗻𝗲𝗲𝗱𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲𝗶𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗲𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. The resulting products ate those which don’t even serve half the population well. Or worse, actively disadvantage them. STEM and AI-related fields are where the highest-paying, most secure and most influential jobs are being created. With fewer women in these roles, the gender wealth gap widens. Then there’s the perennial hidden bias of the recruitment process that women have to fight. Some AI recruitment tools already filter out resumes with “women-coded” language. In developing countries, the danger runs equally deep. A limited digital access means the gap widens even more. Women lack access to training, tools and a chance to compete. Therefore, with automation of manual repetitive jobs, it is not simply about who gets hired or who remains in job. It is more about who gets to participate in the economy of the future. The brunt of this will be borne mostly by women. 𝗪𝗵𝗲𝗻 𝘄𝗼𝗺𝗲𝗻 𝗮𝗿𝗲 𝗲𝘅𝗰𝗹𝘂𝗱𝗲𝗱 𝗳𝗿𝗼𝗺 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝘆 𝗿𝗶𝘀𝗸 𝗹𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘃𝗼𝗶𝗰𝗲. 𝗜𝗳 𝘄𝗲 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴, 𝘄𝗲 𝘄𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗽𝗲𝗿𝗽𝗲𝘁𝘂𝗮𝘁𝗲 𝗶𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘄𝗲 𝘄𝗶𝗹𝗹 𝗔𝗨𝗧𝗢𝗠𝗔𝗧𝗘 𝗜𝗧 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝘂𝘁𝘂𝗿𝗲. #womenintech #futureofwork #AI #digitalinclusion Anthropia Margareth Goldenberg

The #AI revolution is here, but is it inclusive? While AI is reshaping economies, workforce dynamics, and #career trajectories, #gender disparities persist—particularly in innovation and #leadership roles. A new white paper developed by the World Economic Forum with LinkedIn highlights a critical reality: economies advancing in AI without diversity risk reinforcing economic divides. Talent pipelines remain uneven, and women innovators are concentrated in just a few economies. Meanwhile, women are more likely to hold roles that AI disrupts rather than augments—putting them at a disadvantage. But there’s hope. LinkedIn data shows that the gender gap in AI talent is narrowing across 74 of 75 economies. Female AI talent is growing, and underreporting could mean the pool is even larger. This presents a key opportunity: with 99% of Fortune 500 companies already using automation in hiring, we must ensure AI enhances, rather than hinders, gender equity. The solution? Companies embedding gender considerations into AI strategies can unlock broader innovation and economic resilience. Equitable AI development isn’t just about fairness—it’s about creating a more robust, future-ready workforce. How is your industry ensuring AI benefits diverse talent to foster inclusive growth, gender parity, and better opportunities for all? #GenderEquality #Women in #STEAM

What if AI could finally close the care gap in women's health? We're witnessing a silent revolution in healthcare technology. AI-driven platforms are stepping in where traditional systems have fallen short, particularly for women and mental health services. Think about it: personalized mood tracking that identifies patterns before you do. Crisis intervention available at 3 AM when therapist offices are closed. Menstrual cycle analysis that actually predicts irregularities with remarkable accuracy. These technologies aren't just convenient alternatives. They represent critical lifelines for underserved communities where healthcare access remains limited or stigmatized. A female colleague shared with me last week about how she thinks our AI solution can connect rural and semi-urban women to specialists they would never have accessed otherwise. Every tech application initially faces rejection – but this "failure" can prove invaluable. It has revealed similar technologies to learn from and guide our development in more innovative directions. The potential impact extends far beyond individual care. These AI solutions could fundamentally transform community health outcomes by providing consistent, stigma-free monitoring and intervention. Traditional healthcare systems weren't designed with women's unique needs in mind. AI offers us a chance to rebuild from the ground up – creating systems that actually understand cyclical health patterns and gender-specific concerns. The most promising aspect? Democratized access. When quality healthcare becomes available through a smartphone, geographic and economic barriers begin to crumble. Have you encountered AI tools addressing women's health or mental wellness? The technological landscape is evolving rapidly, and I'm curious about your experiences with these emerging solutions. Build healthcare that works for everyone. The technology exists – now we need to ensure it reaches those who need it most.

There is now global, near-universal evidence of a gender gap in generative AI use. According to an Harvard Business Review meta-analysis across countries, sectors, education levels, and occupations, women are consistently 20–25% less likely than men to use AI tools. Importantly, this gap persists even when access, training, and job role are held constant. Why? I recently listened to – and highly recommend – a thought-provoking podcast conversation that explores what is going on here (link posted in the comments). Spoiler alert: the answer may not be what many assume. This gap is not because women lack digital skills, curiosity, or capacity. And it’s not simply an access problem. It is a symptom of deeper structural and experiential factors, like: ⚠️ Rational skepticism grounded in unreliable performance and bias. Many AI systems often demonstrably work less well for women, particularly in high‑stakes contexts like pay negotiation, credibility, and evaluation. When tools are trained on skewed data and designed by homogenous teams, uneven performance isn’t theoretical; it’s lived. ⚠️ Unequal professional and reputational risk. Women face greater scrutiny for how they work. Using AI can carry higher perceived downside for women than men, such as greater penalties for mistakes, greater risk of being seen as less competent, and fewer permissions to “experiment in public.” ⚠️ Time poverty and invisible labor. Learning, prompting, correcting, and validating AI outputs often adds work rather than removes it. For many women already carrying disproportionate paid and unpaid labor, AI adoption can feel like a second or third job, and a cumbersome one if you’re arguing with AI over gender-biased responses, as the podcast hosts hilariously described. In my work on AI adoption and readiness globally, these dynamics are often more acute in developing and emerging markets, where gender inequality in labor markets, social norms, and access to recourse is wider. To be sure, I don’t agree with every perspective or example raised in the podcast, and I’m intentionally cautious about overly alarmist takes. I do believe many of these challenges are solvable. Bias can be reduced. Incentives can be realigned. Systems can be designed to earn trust. But the framing shift they propose feels not just timely, but essential: Women using AI less (or differently) should be treated not as a skills failure, but as a signal. A signal about trust, incentives, design quality, and unequal risk. If we want equitable, inclusive AI adoption, we won’t get there by telling women to “catch up” or trying to convince them that these systems perform perfectly or are bias-free. We’ll get there by taking these signals seriously, and heed them as we intentionally build AI systems, products, and tools that are worthy of adoption by everyone. #AIAdoption #GenderDigitalDivide #ResponsibleAI #FutureOfWork

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