Unequal by Design: How AI Disruption Falls Hardest on Women Workers

The numbers arrived in February 2026, tucked inside a statistical bulletin from Quebec's Institut de la statistique, and they carried a weight that belied their tidy presentation. Women in the province were more likely than men to be exposed to artificial intelligence in their jobs: 71 per cent versus 49 per cent. The gap was not a rounding error. It was a chasm, one that reflected decades of occupational sorting, educational channelling, and structural inequality that predated the first neural network by generations. And while the figures came from a single Canadian province, they echoed findings from the International Monetary Fund, the International Labour Organization, the OECD, and the World Economic Forum, all of which have documented the same basic reality: the AI revolution is not arriving on equal terms.

This is not an abstract policy concern. It is a live question about who benefits and who gets left behind as the most consequential technology of the 21st century reshapes the global economy. The answer, if current trends continue unchallenged, is that women will bear a disproportionate share of the disruption while capturing a smaller share of the gains. Understanding why requires looking beyond the algorithms themselves and into the labour markets, education systems, care economies, and policy frameworks that determine who works where, who trains for what, and who has the time and resources to adapt when the ground shifts.

Where the Numbers Come From

The 71 per cent versus 49 per cent figures originate from work by the Institut de la statistique du Quebec, published on 3 February 2026 [1]. The analysis applied a complementarity-adjusted AI occupational exposure index, drawing on methodology developed by Mehdi and Morissette for Statistics Canada [2] and grounded in the IMF's broader framework for measuring AI exposure across occupations. The index distinguishes between high-complementarity exposure (where AI is likely to augment human work) and low-complementarity exposure (where AI could replace or fundamentally transform tasks). Women scored higher than men on both dimensions: 35 per cent versus 26 per cent for high-complementarity roles, and 36 per cent versus 23 per cent for low-complementarity roles.

These are not outlier results. The IMF's January 2024 staff discussion note, “Gen-AI: Artificial Intelligence and the Future of Work,” found that in most countries, women tend to be employed in high-exposure occupations more than men [3]. In advanced economies, roughly 60 per cent of all employment sits in occupations highly exposed to AI, and women are overrepresented in that pool. The Kenan Institute of Private Enterprise at the University of North Carolina calculated that nearly 80 per cent of women in the United States workforce occupy roles with significant generative AI exposure, compared with 58 per cent of men [4]. Even accounting for the fact that men outnumber women in the total American workforce (84.21 million versus 74.08 million), more women (58.87 million) than men (48.62 million) sit in the 15 most AI-affected occupational categories.

The ILO's refined Global Index of Occupational Exposure, published in May 2025 with Poland's National Research Institute (NASK), sharpened the picture further [5]. In high-income countries, 9.6 per cent of female employment falls into the highest-risk category for AI-driven task automation, nearly three times the 3.5 per cent share for men. Globally, the figures are 4.7 per cent for women and 2.4 per cent for men. And the ILO's lead researcher on the study, Pawel Gmyrek, put the central question plainly: “The key question isn't whether AI will change work. It's who will benefit from those changes.”

The OECD reinforced these findings in its December 2024 policy brief, “Algorithm and Eve: How AI Will Impact Women at Work” [21]. The report found that while female and male workers face roughly the same overall occupational exposure to AI, the nature of that exposure differs profoundly. Male AI users were more likely to be managers and professionals whose work would be augmented by the technology, whilst female AI users were more likely to be clerical support or service workers in roles facing disruption. LinkedIn data cited in the report showed that men hold 54 per cent of AI-augmented occupations, whilst women make up 57 per cent of those in roles likely to be disrupted. The distinction between augmentation and disruption is not semantic. It is the difference between a tool that enhances your productivity and one that renders your role obsolete.

The Occupational Sorting Machine

The statistical disparity did not materialise from thin air. It is a direct product of how labour markets have been organised for decades. Women are disproportionately concentrated in clerical, administrative, customer service, and data-processing roles. These are precisely the categories that generative AI handles with the greatest ease. The ILO found that 24 per cent of clerical tasks are highly exposed to AI automation, with an additional 58 per cent facing medium-level exposure [5]. Data entry clerks, payroll clerks, typists, and accounting clerks sit at the apex of vulnerability.

The underlying mechanism is straightforward. A higher proportion of working women hold white-collar positions (approximately 70 per cent) compared with men (approximately 50 per cent), according to analysis by Mark McNeilly at the Kenan Institute [4]. Men are more evenly distributed between white-collar and blue-collar work, and blue-collar roles, which involve physical manipulation, spatial navigation, and on-site presence, are substantially less susceptible to automation by current AI systems. The construction worker, the electrician, and the plumber face different kinds of labour market pressure, but generative AI is not one of them.

This occupational sorting is not a matter of individual choice operating in a vacuum. It reflects decades of gendered educational pathways, hiring practices, workplace cultures, and social expectations. Claudia Goldin, the Harvard economist who won the 2023 Nobel Memorial Prize in Economics for her research on women's labour market participation, has documented how technological transitions have repeatedly reshaped the gendered distribution of work [22]. During the shift from the first to the second industrial revolution, new technologies required large numbers of white-collar workers to process orders and keep the books, and women filled many of these emerging office roles as secretaries, stenographers, typists, and telephone operators. A century later, those same categories of work are among the most vulnerable to AI automation. Goldin's research also demonstrated that the bulk of the contemporary gender earnings gap arises not from differences between occupations but from differences within them, largely driven by the unequal division of caregiving responsibilities and the premium placed on inflexible “greedy work” schedules.

Women entered administrative and service professions in large numbers during the second half of the twentieth century, partly because these roles were available and partly because structural barriers kept them out of other fields. The result is a labour market architecture in which the very roles that opened doors for women's economic participation are now among the first to be reshaped by automation.

McKinsey Global Institute projected in 2023 that by 2030, activities accounting for up to 30 per cent of hours currently worked across the American economy could be automated, a trend accelerated by generative AI [6]. Office support and customer service, fields where women are heavily represented, could shrink by approximately 3.7 million and 2 million jobs respectively. Women are 1.5 times more likely than men to need to transition into entirely new occupations. Globally, McKinsey estimated that between 40 million and 160 million women may need to make some form of occupational transition by 2030.

Echoes of Earlier Disruptions

The pattern has precedents. Technological disruptions have consistently distributed their costs unevenly across gender lines, and the transition periods, not the eventual outcomes, have determined who thrives and who falls behind.

During the Industrial Revolution, the shift from home-based production to factory labour fundamentally altered women's economic roles. Before industrialisation, household production gave women a recognised economic function. The factory system relocated production outside the home, and while women and children were employed in textile mills and garment factories, they earned lower wages, faced exploitative conditions, and lacked the political rights to organise effectively [7]. The University of Massachusetts Lowell's Tsongas Industrial History Center documents how women in early American mills worked 12 to 14 hour days for wages that were a fraction of what men earned in comparable roles. Though industrialisation eventually expanded women's participation in paid work, the immediate effect was to deepen economic dependency and entrench occupational hierarchies that persisted for generations.

The computerisation wave of the late twentieth century created a similar dynamic. As personal computers and early automation swept through office environments, many clerical roles held predominantly by women were eliminated or restructured. A January 2026 report from SynED, which applied historical pattern analysis to AI employment disruption, identified a recurring “displacement hump”: job losses are front-loaded at the beginning of a technological transition, accumulate as workers struggle to adapt, and gradually fade as retraining takes hold or affected workers exit the workforce [8]. The critical finding was that only 17 per cent of American manufacturing hubs that experienced automation-driven displacement successfully recovered to prior employment levels, compared with nearly 50 per cent of German hubs, where coordinated retraining and social safety net policies cushioned the blow.

The first Industrial Revolution offers a further cautionary note. As the IMF has observed, productivity grew substantially during the early 1800s in Britain, yet real wages remained flat for approximately 40 years for large sections of the working population [3]. The gains from technological progress were captured by capital owners long before they trickled through to workers. If the AI transition follows a similar pattern, the question of who benefits during the transition period becomes more urgent than the question of long-term economic growth.

The lesson is not that technology inevitably harms women. It is that without deliberate intervention, the costs of transition fall disproportionately on those already occupying the more vulnerable positions in the labour market.

The STEM Pipeline and the AI Talent Gap

If occupational segregation determines who is most exposed to AI disruption, the composition of the AI workforce itself determines who shapes the technology and captures its economic benefits. Here, the gender imbalance is equally stark.

According to the World Economic Forum's March 2025 white paper, “Gender Parity in the Intelligent Age,” women make up just 28.2 per cent of the global STEM workforce, compared with 47 per cent of non-STEM workers [9]. The attrition is severe: while women constitute more than a third of STEM graduates, only 29.6 per cent remain in STEM roles one year after graduation. By the time one reaches the executive suite, a mere 12.2 per cent of STEM C-suite positions are held by women. In 2024, women held 29 per cent of entry-level STEM positions and 24.4 per cent of STEM managerial positions, illustrating a persistent narrowing at each rung of the career ladder [9]. The pipeline does not merely leak; it haemorrhages.

In AI specifically, women represent only 22 per cent of AI talent globally, with even lower representation at senior levels, occupying fewer than 14 per cent of senior executive positions [9]. LinkedIn data analysed for the WEF report showed that in 2018, only 23.5 per cent of professionals listing AI engineering skills were women. By early 2025, that share had risen to 29.4 per cent, narrowing the gap in 74 of 75 economies surveyed. Progress, then, but incremental, and the report noted that women are more likely to underreport AI skills in professional profiles, suggesting the actual talent pool may be somewhat larger than the data indicate.

The underrepresentation of women in AI development has consequences that extend beyond employment statistics. UNESCO's 2024 study, “Bias Against Women and Girls in Large Language Models,” examined GPT-3.5, GPT-2, and Meta's Llama 2, finding unequivocal evidence of gender bias in content generated by each model [10]. Women were described in domestic roles four times as often as men by one model. Female names were associated with words such as “home,” “family,” and “children,” while male names were linked to “business,” “executive,” and “career.” When prompted to generate content intersecting gender with occupation, the models assigned more diverse and professional roles to men, while relegating women to stereotypically undervalued positions. UNESCO Director General Audrey Azoulay warned that “these new AI applications have the power to subtly shape the perceptions of millions of people, so even small gender biases in their content can significantly amplify inequalities in the real world.”

When the people building the systems do not reflect the diversity of the people affected by them, the systems encode and amplify existing biases. According to the most recent data cited in the UNESCO study, women represent only 20 per cent of employees in technical roles at major machine learning companies, 12 per cent of AI researchers, and 6 per cent of professional software developers. The feedback loop is pernicious: biased systems discourage women's participation, which perpetuates homogeneous development teams, which produce biased systems. Researchers have warned of a potential reinforcement cycle in which the current gender gap in AI usage leads to biased AI systems that further discourage women's engagement with the technology [10].

The Care Economy Trap

There is another dimension to this disparity that rarely appears in labour market models but profoundly shapes women's capacity to adapt to technological change: unpaid care work.

The International Labour Organization estimates that unpaid care responsibilities prevent 708 million women from participating in the labour market globally [11]. Among women aged 25 to 54 who are outside the workforce, two-thirds (379 million) cite care responsibilities as the primary reason, compared with only 5 per cent of men in the same position. The OECD's September 2025 report on gender gaps in paid and unpaid work documented how these patterns start early, with girls and boys exposed to gender norms that assign domestic responsibility primarily to women, and persist throughout the life course [12]. Older women face compounded barriers, shouldering unpaid care for elderly relatives whilst also confronting stronger negative perceptions about outdated skills.

This matters enormously for AI transition planning. Reskilling and upskilling programmes require time: time to attend courses, time to practise new skills, time to search for new roles. Women who are already working a “second shift” of unpaid care after their formal employment hours have less of this commodity than anyone. Workers in administrative and clerical roles, those most exposed to AI displacement, frequently lack access to effective retraining, facing structural barriers related to time, cost, and digital literacy [5]. The ILO has specifically noted that women in automation-prone occupations often lack access to the technical training needed to transition to AI-adjacent roles, and that this skills gap is compounded by systemic barriers including discrimination, unconscious bias, and persistent gender pay gaps.

The OECD has noted that emerging AI-related roles disproportionately require advanced education: 77 per cent of new AI-related positions require a master's degree or equivalent advanced training, substantially above the 35 per cent education requirement for the roles they are displacing [13]. For women already constrained by care obligations and unable to pursue extended formal education, this creates a double bind. The jobs disappearing require fewer qualifications than the jobs replacing them, and the people most affected have the least capacity to bridge the gap. Goldin's research underscores this dynamic: the gender pay gap would be considerably smaller if firms did not disproportionately reward individuals who work long and inflexible hours, and the women most likely to need AI reskilling are precisely those whose care responsibilities make inflexible training schedules impossible to accommodate [22].

What Governments and Institutions Are Doing (and What They Are Not)

The policy landscape is uneven. Some governments have begun integrating gender considerations into their AI transition strategies, but comprehensive, gender-sensitive approaches remain the exception rather than the norm.

Singapore's SkillsFuture programme offers one model. Under the Level-Up Programme launched in 2024, all Singaporeans aged 40 and above received a SGD 4,000 SkillsFuture Credit top-up to support mid-career reskilling, with subsidies covering up to 90 per cent of course fees [14]. The programme has been explicitly highlighted as particularly beneficial for women seeking to re-enter the workforce or acquire digital skills. Singapore's employment rate for women aged 25 to 64, at 77 per cent, remains among the highest globally. A separate SGD 1 billion Digital Skills Future Fund, introduced in 2025, targets both young professionals and mid-career workers across AI, cybersecurity, and green technology sectors. In 2023, SkillsFuture empowered over 520,000 individuals, with 95 per cent of credit users directing funds toward industry-specific courses [14].

The European Union has taken a regulatory approach. The EU AI Act, which became applicable in stages from 2024, classifies all AI systems used in recruitment and employment decisions as “high-risk,” subjecting them to stringent requirements for safety, fairness, and transparency [15]. Article 4's AI literacy requirements, applicable from February 2025, mandate that organisations ensure adequate AI literacy across their workforce, explicitly requiring them to account for variations in staff knowledge, experience, and training. As Women in AI and other organisations have noted, this creates a legal imperative to design targeted literacy pathways for women, given that nearly half lack basic awareness of generative AI tools [16]. Notably, 99 per cent of Fortune 500 companies already use automation in their hiring practices, making the regulatory framework for bias prevention in AI-driven recruitment increasingly urgent [9].

In June 2025, the Council of the European Union adopted conclusions calling for targeted efforts to advance gender equality in the AI-driven digital age [17]. The European Institute for Gender Equality has advocated for the integration of gender impact assessments into the AI Act's implementation, and the forthcoming EU Gender Equality Strategy 2026 to 2030 is expected to prioritise gender-sensitive approaches to the digital transition.

Yet significant gaps persist. The AI Act's fundamental rights impact assessment obligations do not apply to all AI systems; private companies deploying AI for internal recruitment decisions may fall outside their scope. And globally, the approach remains fragmented. IMF Managing Director Kristalina Georgieva has repeatedly called for comprehensive social safety nets and retraining programmes for vulnerable workers. “If we don't have thoughtful distribution of benefits [of AI] and inequality grows dramatically, that can break the social fabric in a way that is going to be very unhealthy for the world,” she told Yahoo Finance in January 2024 [3]. At the 2026 World Economic Forum in Davos, Georgieva described AI as a “tsunami hitting the labour market” and urged proactive measures: reskilling youth, bolstering social safety nets, and regulating AI for inclusivity [18].

Designing Reskilling That Actually Works

If the diagnosis is clear, the prescription remains contested. How do you design reskilling and upskilling programmes that genuinely serve the people most affected by AI disruption, rather than simply those best positioned to access existing training infrastructure?

The evidence suggests several principles. First, timing matters. The ILO and the SynED historical analysis both point to the “displacement hump” as the period of greatest vulnerability [5][8]. Programmes that arrive after mass displacement has already occurred are too late. Effective intervention requires anticipatory investment, identifying at-risk occupational categories and building training pathways before roles begin to shrink.

Second, accessibility is non-negotiable. Programmes must account for the care responsibilities, time constraints, and financial limitations that disproportionately affect women. This means flexible scheduling, modular course designs that allow for interrupted study, subsidised or free participation, and integrated childcare provision. Singapore's approach of direct credit top-ups reduces the financial barrier, but time constraints remain a bottleneck that financial subsidies alone cannot solve.

Third, the destination matters as much as the journey. Reskilling into low-wage, precarious roles is not a genuine solution. McKinsey's analysis found that people in the two lowest wage quintiles are up to 10 and 14 times more likely to need to change occupations by 2030 than the highest earners, and these quintiles are disproportionately held by women and people of colour [6]. Effective programmes must connect to roles that offer wage parity or improvement, not simply shuffle workers from one vulnerable category into another.

Fourth, employer accountability is essential. The World Economic Forum's 2025 white paper argued that “companies that fail to integrate gender parity into AI strategy will miss out on half of the available talent, reducing their capacity for innovation and long-term competitiveness” [9]. Saadia Zahidi, Managing Director at the WEF, has emphasised that economies advancing in AI without diversity may face setbacks and inequality, while those attracting diverse talent gain competitive advantage. This is not merely a social justice argument; it is an economic efficiency argument. Companies deploying AI systems should be required to conduct and publish gender impact assessments of their automation decisions, and to invest in retraining for affected workers proportionate to the scale of displacement.

Fifth, AI literacy must become universal, not optional. The gender gap in AI adoption is well documented. Women accounted for just 42 per cent of ChatGPT's approximately 200 million average monthly website visitors between November 2022 and May 2024, and in a recent study, female workers were 20 percentage points less likely to report having used ChatGPT than male workers in the same occupation [21]. Closing this usage gap requires deliberate investment in digital confidence-building, not just technical training, but programmes that demystify AI tools and demonstrate their relevance across a range of professional contexts.

International Perspectives on an Unequal Transition

The gender dimensions of AI disruption vary significantly across income levels and regions. The ILO's data reveal that in high-income countries, 34 per cent of employment is in occupations exposed to generative AI, compared with just 11 per cent in low-income countries [5]. But this does not mean that lower-income economies are immune. In developing countries, only 20 per cent of women have internet access, a fundamental barrier as AI becomes increasingly central to economic participation.

Europe and Central Asia show the highest gender disparities in AI exposure, driven by high female employment in clerical roles and widespread digital adoption [5]. The European Commission's March 2025 Roadmap for Women's Rights acknowledged this dynamic, and EIGE Director Carlien Scheele has warned that AI is “still nascent enough to be 'rewired' through gender-responsive approaches,” but that the window for action is narrowing [15].

In the United States, the intersection of gender with race creates additional layers of vulnerability. McKinsey's 2024 “Women in the Workplace” report found that for every 100 men promoted to manager, only 81 women receive the same opportunity [19]. For Black women, the figure drops to 54; for Latinas, to 65. At current rates of progress, it will take 48 years for women in senior corporate positions to reflect their population share. These existing inequalities compound the AI transition's differential impacts: women of colour are disproportionately represented in the low-wage service and clerical roles most exposed to automation, and they face the steepest barriers to retraining and advancement.

The WEF's Global Gender Gap Report 2025 estimates that achieving full global gender parity will take 123 years at current rates of progress [20]. Despite women representing 41.2 per cent of the global workforce, only 28.8 per cent reach senior leadership roles. Between 2015 and 2024, the share of women in top-management positions rose from 25.7 per cent to 28.1 per cent, but momentum has slowed since 2022, and the gap between mid-level and top-level leadership has stalled at 5.4 percentage points [20]. The AI transition is not creating these inequalities from scratch. It is amplifying and accelerating them, and without coordinated international action, it threatens to add decades to an already glacial trajectory toward parity.

Building the Architecture of an Equitable Transition

The evidence does not support fatalism. It supports urgency. The technology is here, the displacement is beginning, and the policy tools exist. What is missing is the political will to deploy them at scale and with the specificity that the problem demands.

An equitable AI transition requires action across multiple dimensions simultaneously. Care infrastructure must be expanded, not as a social nicety, but as an economic prerequisite for workforce adaptation. The OECD recommends investment in affordable childcare and long-term care, well-paid parental leave with “use it or lose it” provisions, flexible working arrangements, and improved pay and formalisation for care-giving professions [12]. These are not peripheral to the AI transition. They are foundational, because without them, millions of women will lack the time and resources to reskill.

Education systems must be reformed to break the pipeline leakage that sees women leaving STEM at every stage of their careers. This means addressing not just access but retention: tackling workplace discrimination, closing gender pay gaps, and creating promotion pathways that do not penalise caregiving interruptions. The WEF noted that women aged 16 to 28 now represent 45.7 per cent of the workforce, a demographic dividend that will only materialise if these younger women can build sustainable careers rather than following the same attrition patterns as their predecessors [9].

AI governance frameworks must embed gender equity from the outset. The EU AI Act's high-risk classification for employment AI is a necessary start, but its scope must be broadened to cover all AI systems with significant workforce impacts, including those deployed by private companies for internal automation decisions. Gender impact assessments should be mandatory, not aspirational, and the results should be public. UNESCO's framework offers a template, calling for ring-fenced funding for gender-parity schemes in companies, financial incentives for women's entrepreneurship, and targeted investment in programmes that increase girls' and women's participation in STEM and ICT disciplines [10].

And the AI industry itself must change. With women comprising just 22 per cent of AI talent and only 6 per cent of professional software developers, the systems being built reflect a narrow slice of human experience. Targeted recruitment, retention programmes, and funding for women-led AI ventures are not charitable gestures. They are corrective measures for a market failure that produces biased technology and excludes half the population from the most consequential industry of the century.

Kristalina Georgieva was right to call AI a tsunami. Tsunamis do not discriminate by gender, but the infrastructure that determines who survives them does. The 71 per cent versus 49 per cent gap is not a fixed feature of the technology. It is a feature of the society into which the technology is being deployed. And societies, unlike algorithms, can choose to change.

References and Sources

  1. Institut de la statistique du Quebec. (2026, February 3). “The Majority of Occupations in Quebec Are Highly Exposed to Artificial Intelligence.” https://statistique.quebec.ca/en/communique/majority-occupations-quebec-highly-exposed-artificial-intelligence

  2. Mehdi, T. and Morissette, R. (2024). “Experimental Estimates of Potential Artificial Intelligence Occupational Exposure in Canada.” Statistics Canada.

  3. International Monetary Fund. (2024, January 14). “Gen-AI: Artificial Intelligence and the Future of Work.” Staff Discussion Note SDN/2024/001. https://www.imf.org/en/publications/staff-discussion-notes/issues/2024/01/14/gen-ai-artificial-intelligence-and-the-future-of-work-542379

  4. Kenan Institute of Private Enterprise, University of North Carolina. (2023). “Will Generative AI Disproportionately Affect the Jobs of Women?” https://kenaninstitute.unc.edu/kenan-insight/will-generative-ai-disproportionately-affect-the-jobs-of-women/

  5. International Labour Organization and NASK. (2025, May). “Generative AI and Jobs: A Refined Global Index of Occupational Exposure.” ILO Working Paper 140. https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure

  6. McKinsey Global Institute. (2023, July). “Generative AI and the Future of Work in America.” https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

  7. Tsongas Industrial History Center, University of Massachusetts Lowell. “The Role of Women in the Industrial Revolution.” https://www.uml.edu/tsongas/barilla-taylor/women-industrial-revolution.aspx

  8. SynED. (2026, January 9). “New Report Applies Historical Pattern Analysis to AI Employment Disruption.” https://syned.org/2026/01/09/new-report-applies-historical-pattern-analysis-to-ai-employment-disruption/

  9. World Economic Forum and LinkedIn. (2025, March). “Gender Parity in the Intelligent Age.” White Paper. https://www.weforum.org/publications/gender-parity-in-the-intelligent-age-2025/

  10. UNESCO. (2024, March). “Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls in Large Language Models.” https://unesdoc.unesco.org/ark:/48223/pf0000388971

  11. International Labour Organization. “Unpaid Care Work Prevents 708 Million Women from Participating in the Labour Market.” https://www.ilo.org/resource/news/unpaid-care-work-prevents-708-million-women-participating-labour-market

  12. OECD. (2025, September). “Gender Gaps in Paid and Unpaid Work Persist.” https://www.oecd.org/en/publications/gender-gaps-in-paid-and-unpaid-work-persist_25a6c5dc-en/full-report.html

  13. OECD. (2024, December). “Training Supply for the Green and AI Transitions.” https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/12/training-supply-for-the-green-and-ai-transitions_e75ff953/7600d16d-en.pdf

  14. SkillsFuture Singapore. (2024). “SkillsFuture Level-Up Programme.” https://www.skillsfuture.gov.sg/

  15. European Commission. (2024). “Regulation (EU) 2024/1689: The Artificial Intelligence Act.” Official Journal of the European Union.

  16. Women in AI. (2025). “Mind the Gap: AI Literacy Requirements Under the EU AI Act and the Gender Divide.” https://www.womeninai.co/post/mind-the-gap-ai-literacy-requirements-under-the-eu-ai-act-and-the-gender-divide

  17. Council of the European Union. (2025, June 19). “Council Calls for Targeted Efforts to Advance Gender Equality in the AI-Driven Digital Age.” https://www.consilium.europa.eu/en/press/press-releases/2025/06/19/council-calls-for-targeted-efforts-to-advance-gender-equality-in-the-ai-driven-digital-age/

  18. TIME Magazine. (2026, January). “The IMF's Kristalina Georgieva on the AI 'Tsunami' Hitting Jobs.” https://time.com/collections/davos-2026/7339218/ai-trade-global-economy-kristalina-georgieva-imf/

  19. McKinsey & Company. (2024). “Women in the Workplace 2024: The 10th Anniversary Report.” https://www.mckinsey.com/featured-insights/diversity-and-inclusion/women-in-the-workplace

  20. World Economic Forum. (2025). “Global Gender Gap Report 2025.” https://www.weforum.org/publications/global-gender-gap-report-2025/

  21. OECD. (2024, December). “Algorithm and Eve: How AI Will Impact Women at Work.” https://www.oecd.org/en/publications/2024/12/algorithm-and-eve_0e889c45.html

  22. Goldin, C. (2023). “Career and Family: Women's Century-Long Journey toward Equity.” Princeton University Press. Nobel Prize in Economics Citation: https://www.nobelprize.org/prizes/economic-sciences/2023/goldin/facts/

Tim Green

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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