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   "site": "International Labour Organization",
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   "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization",
   "snip": "Methodology: Updated 2025 ILO index combining 52,558 task automation data points, expert surveys, and Delphi discussions across ISCO-08 classifications, establi...",
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   "title": "Generative AI and jobs: A 2025 update | International Labour Organization",
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   "title": "Generative AI and Jobs - A Refined Global Index of Occupational Exposure - Scribd",
   "snip": "ILO Working Paper 140. May / 2025. XX Generative AI and Jobs. A Refined Global Index of Occupational Exposure. Authors / Pawel Gmyrek, Janine Berg, Karol Kamińs...",
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 "transcript": "[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14), 2026-10-29. The paste is the Overview expanded (the 'AI Mode Conversation' view, with the source cards that follow the answer); the 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted. Supplied as an attached file.]\n\n[QUERENT] ILO Generative AI and Jobs index\n\n[SYSTEM] The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).\n\nKey Findings of the Index\n\n• Global Exposure Rate: About 24% (1 in 4) of global workers are in occupations with some level of exposure to generative AI (GenAI).\n• Highest Exposure Category (Gradient 4): Only 3.3% of global employment falls into the highest exposure category where a large majority of tasks can be heavily impacted.\n• Transformation vs. Replacement: The research emphasizes job transformation rather than widespread job elimination or mass redundancy, as very few occupations consist entirely of automatable tasks.\n• Income Disparities: High-income countries (HICs) show a 34% total GenAI exposure rate, compared to just 11% in low-income countries (LICs), reflecting differences in economic structures and digital adoption.\n• Gender Divide: Women are nearly twice as likely to hold jobs exposed to GenAI as men (roughly 29% vs. 16% across sampled countries), largely because women are more heavily represented in clerical, administrative, and routine information-processing roles.\n• Most Exposed Occupations: Clerical support workers, administrative staff, secretaries, receptionists, data entry operators, and translators face the highest exposure.\n• Least Exposed Occupations: Manual trades, construction, agriculture, and heavy manufacturing involve physical and non-routine tasks with very low GenAI exposure.\n\nMethodology\n\n• Task-Level Assessment: Evaluates nearly 30,000 to over 52,000 individual task data points across the International Standard Classification of Occupations (ISCO-08) framework.\n• Four-Gradient Framework: Combines worker surveys, expert Delphi panel inputs, and large-scale AI assessments to categorize jobs into four distinct exposure gradients based on mean exposure scores and task variability.\nWould you like to explore:\n• Specific exposure scores for a particular occupation or industry?\n• How GenAI impacts administrative versus technical roles?\n• Policy recommendations from the ILO for workforce upskilling?\nInternational Labour Organization\nGenerative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization\nMethodology: Updated 2025 ILO index combining 52,558 task automation data points, expert surveys, and Delphi discussions across ISCO-08 classifications, establi...\nInternational Labour Organization\nGenerative AI and jobs: A 2025 update | International Labour Organization\nMethodology: Updated 2025 ILO framework combining human and AI insight at the 6-digit occupational level across nearly 30,000 tasks, defining four gradients of ...\nLinkedIn·Grace S. Thomson\nA Refined Global Index of Occupational Exposure | Grace S. Thomson\nGrace S. Thomson's Post Grace S. Thomson 1y This International Labour Organization report on Generative AI and Jobs [May 2025] presents a comprehensive assessme...\nNASK – Państwowy Instytut Badawczy\nILO-NASK Indeks\nMethodology: Developed by ILO and NASK to assess generative AI's task-level impact on work, updating the ILO's 2023 Global Index using advanced AI and worker fa...\nThe People Space\nWhat ILO's Global Index tells us about GenAI and jobs | The People Space\nThe results bring clarity to what AI can and can't do in the workplace right now. * One in four jobs touched by GenAI but transformation, not replacement, is th...\nYouTube·International Labour Organization\n11m\nGenerative AI and Jobs: A global analysis of potential effects on job quantity and quality.\nYouTube·International Labour Organization\n3m\nHow is generative AI reshaping gender inequalities at work?\nwww.scribd.com\nGenerative AI and Jobs - A Refined Global Index of Occupational Exposure - Scribd\nILO Working Paper 140. May / 2025. XX Generative AI and Jobs. A Refined Global Index of Occupational Exposure. Authors / Pawel Gmyrek, Janine Berg, Karol Kamińs...",
 "transcript_raw": "The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).\n\nKey Findings of the Index\n\n• Global Exposure Rate: About 24% (1 in 4) of global workers are in occupations with some level of exposure to generative AI (GenAI).\n• Highest Exposure Category (Gradient 4): Only 3.3% of global employment falls into the highest exposure category where a large majority of tasks can be heavily impacted.\n• Transformation vs. Replacement: The research emphasizes job transformation rather than widespread job elimination or mass redundancy, as very few occupations consist entirely of automatable tasks.\n• Income Disparities: High-income countries (HICs) show a 34% total GenAI exposure rate, compared to just 11% in low-income countries (LICs), reflecting differences in economic structures and digital adoption.\n• Gender Divide: Women are nearly twice as likely to hold jobs exposed to GenAI as men (roughly 29% vs. 16% across sampled countries), largely because women are more heavily represented in clerical, administrative, and routine information-processing roles.\n• Most Exposed Occupations: Clerical support workers, administrative staff, secretaries, receptionists, data entry operators, and translators face the highest exposure.\n• Least Exposed Occupations: Manual trades, construction, agriculture, and heavy manufacturing involve physical and non-routine tasks with very low GenAI exposure.\n\nMethodology\n\n• Task-Level Assessment: Evaluates nearly 30,000 to over 52,000 individual task data points across the International Standard Classification of Occupations (ISCO-08) framework.\n• Four-Gradient Framework: Combines worker surveys, expert Delphi panel inputs, and large-scale AI assessments to categorize jobs into four distinct exposure gradients based on mean exposure scores and task variability.\nWould you like to explore:\n• Specific exposure scores for a particular occupation or industry?\n• How GenAI impacts administrative versus technical roles?\n• Policy recommendations from the ILO for workforce upskilling?\nInternational Labour Organization\nGenerative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization\nMethodology: Updated 2025 ILO index combining 52,558 task automation data points, expert surveys, and Delphi discussions across ISCO-08 classifications, establi...\nInternational Labour Organization\nGenerative AI and jobs: A 2025 update | International Labour Organization\nMethodology: Updated 2025 ILO framework combining human and AI insight at the 6-digit occupational level across nearly 30,000 tasks, defining four gradients of ...\nLinkedIn·Grace S. Thomson\nA Refined Global Index of Occupational Exposure | Grace S. Thomson\nGrace S. Thomson's Post Grace S. Thomson 1y This International Labour Organization report on Generative AI and Jobs [May 2025] presents a comprehensive assessme...\nNASK – Państwowy Instytut Badawczy\nILO-NASK Indeks\nMethodology: Developed by ILO and NASK to assess generative AI's task-level impact on work, updating the ILO's 2023 Global Index using advanced AI and worker fa...\nThe People Space\nWhat ILO's Global Index tells us about GenAI and jobs | The People Space\nThe results bring clarity to what AI can and can't do in the workplace right now. * One in four jobs touched by GenAI but transformation, not replacement, is th...\nYouTube·International Labour Organization\n11m\nGenerative AI and Jobs: A global analysis of potential effects on job quantity and quality.\nYouTube·International Labour Organization\n3m\nHow is generative AI reshaping gender inequalities at work?\nwww.scribd.com\nGenerative AI and Jobs - A Refined Global Index of Occupational Exposure - Scribd\nILO Working Paper 140. May / 2025. XX Generative AI and Jobs. A Refined Global Index of Occupational Exposure. Authors / Pawel Gmyrek, Janine Berg, Karol Kamińs...",
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  "basis": "reached: The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).; The index is composed as itself, by its full title and working-paper number, bound to the ILO, with its headline findings (24%, 3.3% in Gradient 4, 34% HICs vs 11% LICs, clerical highest, transformation over redundancy) and its four-gradient method: same.",
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   "notes": "Claim boundary present: the index's claims are carried as exposure ('some level of exposure') and its transformation-not-redundancy finding at its own force; the 'upper threshold'/'not the actual impact on employment' and static-snapshot limits are not stated. One figure is foreign to the records: 'Women are nearly twice as likely to hold jobs exposed to GenAI as men (roughly 29% vs. 16% across sampled countries)' (the records give 4.7% female vs 2.4% male in the highest gradient; 29% and 16% occur in neither saved source); the 'Least Exposed Occupations' bullet is likewise not in the saved text. Method conflated: 'nearly 30,000 to over 52,000 individual task data points across ... (ISCO-08)' joins the 29,753 Polish tasks and the 52,558 survey data points and places them in ISCO-08. Lineage absent: 'updated assessment' implies the 2023 index without naming it (lineage needs its token by word). 'ILO Working Paper 140' is a registered alias and fills the name atom only; no DOI or ISBN. No date (20 May 2025), no CC BY 4.0, no Observatory, no stated purpose (policy understanding, social dialogue). No inversion, no standing substitution."
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 "analysis": "An exposure index is composed at its headline grain, with one figure the paper does not hold and without the paper's own statement of what exposure is not. The record's negative tags (not an observed outcome; not a forecast) are what separate this resource from the employment trackers beside it in the census.",
 "d": "THE ILO GENERATIVE AI AND JOBS INDEX, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).' Seven findings (24% of workers exposed; 3.3% in the highest gradient; transformation over replacement; 34% in high-income against 11% in low-income countries; women nearly twice as exposed as men, 'roughly 29% vs. 16%'; the most and least exposed occupations) and the method (task data points, four gradients). Eight cards, two of them the ILO's.",
 "d_full": "THE ILO GENERATIVE AI AND JOBS INDEX, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'The International Labour Organization (ILO) published its updated assessment in the report \"Generative AI and Jobs: A Refined Global Index of Occupational Exposure\" (ILO Working Paper 140).' Seven findings (24% of workers exposed; 3.3% in the highest gradient; transformation over replacement; 34% in high-income against 11% in low-income countries; women nearly twice as exposed as men, 'roughly 29% vs. 16%'; the most and least exposed occupations) and the method (task data points, four gradients). Eight cards, two of them the ILO's.",
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  "THE INDEX HELD. Working Paper 140, 24%, 3.3% in the highest gradient, 34% against 11% by income group and the transformation reading ('most jobs are more likely to be transformed than made redundant') are the record's. Checked 2026-10-29 against the Labor Vector census record of the same day and the sources it saved.",
  "A GENDER FIGURE NOT IN THE PAPER. The paper states that women are more affected than men; its own figures for the highest gradient are 4.7% of female and 2.4% of male employment worldwide. 'roughly 29% vs. 16% across sampled countries' is in no saved source; both numbers are in the paper as 2023 category shares: 29.2% is the 2023 index's combined share of augmentation and 'the big unknown', and 16.2% the share of 'the big unknown' alone (the same paragraph).",
  "TWO COUNTS RUN TOGETHER. 'nearly 30,000 to over 52,000 individual task data points' joins the 29,753 Polish tasks scored and the 52,558 data points; the record keeps them as two measures, and so does The People Space's card, which gives both in one sentence; the join into one range is the composition's.",
  "THE LIMITS ABSENT. The estimates measure exposure and give an upper threshold of potential, and the paper says they are no measure of actual employment effects; a static snapshot at the start of 2025; a survey run in Poland, comparable only within each occupational group. The composition's 'can be heavily impacted' stays inside exposure, and gives no limit."
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