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The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/ilo-genai-exposure-index/", "research": { "google": "https://www.google.com/search?q=Generative+AI+and+Jobs%3A+A+Refined+Global+Index+of+Occupational+Exposure", "google_non": "https://www.google.com/search?q=%22Generative+AI+and+Jobs%3A+A+Refined+Global+Index+of+Occupational+Exposure+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+Generative+AI+and+Jobs%3A+A+Refined+Global+Index+of+Occupational+Exposure%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CGenerative+AI+and+Jobs%3A+A+Refined+Global+Index+of+Occupational+Exposure+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+Generative+AI+and+Jobs%3A+A+Refined+Global+Index+of+Occupational+Exposure%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure", "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space", "status": "composed 2026-10-29 under v0.9 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledger unaudited", "compression": { "title": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure", "lede": "is the ILO's index of occupational exposure to generative AI, made with NASK (Working Paper 140, 20 May 2025; the paper): an exposure estimate of tasks' technical feasibility for automation that observes no performed work, its figures an upper threshold, not an impact (Labor Vector record; the paper)." }, "sentences": [ { "n": 1, "para": 0, "text": "Generative AI and Jobs: A Refined Global Index of Occupational Exposure is an index of occupational exposure to generative AI, \"published as a working paper with a research brief\" (Labor Vector record), which in its authors' words \"updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI)\" (the paper) and which, on the ILO's page, \"refines the global measurement of occupational exposure to generative AI by combining task-level data, expert input, and AI model predictions\" (ILO publication page).", "claims": [ "F1", "F2", "LV-01", "LV-02" ] }, { "n": 2, "para": 0, "text": "Its brief is \"Generative AI and jobs: A 2025 update\" (ILO brief page); NASK's page calls it the ILO–NASK Index, \"developed to better understand how generative AI may affect work at the task level\" (NASK); and the Labor Vector record lists among its names \"ILO Working Paper 140\" and the Observatory's \"AI impact on jobs\" (Labor Vector record).", "claims": [ "F3", "F4", "LV-03" ] }, { "n": 3, "para": 0, "text": "The Labor Vector record classes the evidence it brings as an \"exposure estimate\" and places it at the intersection of the \"Distribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains\", under the question it puts to every resource it holds: \"Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?\" (Labor Vector record).", "claims": [ "LV-05", "LV-06", "LV-07", "LV-08" ] }, { "n": 4, "para": 1, "text": "The paper gives its makers as the ILO's Research department \"partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland\" (the paper); NASK's page gives the report as prepared by the NASK Analysis and Research Office, the ILO having \"played an active role in the preparation of this report\" (NASK); and the Labor Vector record names the ILO \"with NASK and the Polish Ministry of Family, Labour and Social Policy (expert group)\" (Labor Vector record).", "claims": [ "F5", "F6", "LV-04" ] }, { "n": 5, "para": 1, "text": "Eight authors are listed on the ILO's publication page, Balint Nafradi among them, and seven, without him, in the paper's own title block (ILO publication page; the paper), the record following the eight (Labor Vector record).", "claims": [ "F7", "F8", "LV-04" ] }, { "n": 6, "para": 1, "text": "Its lineage is the ILO's first index of 2023, \"the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter)\", which scored ISCO-08 task descriptions with GPT-4 (the paper; Labor Vector record, founded), and an ILO video on the rail carries that paper's title, \"Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.\" (YouTube card, title only).", "claims": [ "F9", "F10", "LV-39" ] }, { "n": 7, "para": 2, "text": "Its method opens with three LLMs \"available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5\", each task scored from 0, where \"the task cannot be automated\", to 1, where \"the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator)\" (the paper).", "claims": [ "F13", "F14" ] }, { "n": 8, "para": 2, "text": "A survey of 1,640 people employed in Poland then scored a representative 2,861 of the 29,753 tasks of the Polish classification, 52,558 data points, each group's tasks \"assessed by individuals working\" in it (the paper), and the record keeps these counts apart, beside 3,265 ISCO-08 tasks rescored and 112 occupations classed as exposed (Labor Vector record), as the Capture Registry finds the composition at the name does not (Capture Registry, 2026-10-29).", "claims": [ "F11", "F12", "LV-16", "CAP-1-05" ] }, { "n": 9, "para": 2, "text": "International experts, for whom \"expertise in AI design or development is not a requirement\", compared the scores in \"several rounds of Delphi-style discussions\"; the human scores became a repository from which \"an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08\" was built, its ISCO-08 and Polish predictions correlating at 0.92, \"demonstrating exceptional stability of predictions\" in the authors' words (the paper; Labor Vector record, method).", "claims": [ "F15", "F16", "F17", "F18", "LV-14" ] }, { "n": 10, "para": 2, "text": "An occupation is classed by \"two moments of distribution: occupational mean and standard deviation (SD) of task-level scores\" (the paper) into \"four progressively increasing gradients of GenAI exposure\" (ILO brief page), Gradient 4 holding \"the highest share of tasks exposed to potential GenAI-driven automation\" with \"a high consistency of this exposure across tasks\"; employment shares are then estimated for \"the 112 occupations identified as exposed\" (the paper; Labor Vector record, unit).", "claims": [ "F19", "F20", "F21", "F22", "LV-09" ] }, { "n": 11, "para": 3, "text": "Globally \"one in four workers are in an occupation with some GenAI exposure\" and \"3.3% of global employment falls into the highest exposure category\", \"female (4.7%) and male employment (2.4%)\" (the paper; Labor Vector record, findings); the Capture Registry finds that the composition at the name, a new address where no card cites the record, names the paper and holds these figures, checked against the census record and its saved sources (Capture Registry, 2026-10-29).", "claims": [ "F23", "LV-17", "LV-18", "CAP-1-01", "CAP-1-02", "CAP-1-08", "CAP-1-11" ] }, { "n": 12, "para": 3, "text": "Exposure rises with income, \"11% of total employment in LICs vs 34% in HICs\", and the gap between the sexes with it: in high-income countries \"28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment\", \"9.6% in gradient 4\", and in Europe and Central Asia 39% of female employment against 26% of male (the paper; Labor Vector record).", "claims": [ "F24", "F25", "F26", "LV-19" ] }, { "n": 13, "para": 3, "text": "The paper reads these patterns as reflecting \"occupational structures\" and where roles \"such as clerical, financial, and customer service jobs\" are concentrated (the paper); The People Space reads the gender gap as \"the gendered distribution of clerical and customer-facing roles\" and the income gap as \"shaped by infrastructure, digital access and workforce structure\" (The People Space); an ILO video on the rail is titled \"How is generative AI reshaping gender inequalities at work?\" (YouTube card, title only).", "claims": [ "F27", "F28", "F29", "F71" ] }, { "n": 14, "para": 3, "text": "The Capture Registry finds the composition's gender figure, \"roughly 29% vs. 16% across sampled countries\", in no saved source, \"the paper's 29.2%\" being \"the 2023 index's combined share of augmentation and 'the big unknown'\" (Capture Registry, 2026-10-29); the paper gives the 2023 shares as 13% \"augmentation\" and \"16.2% of employment in “the big unknown”\", against 20.5% for its own Gradients 1–3 (the paper).", "claims": [ "CAP-1-03", "CAP-1-04", "F30" ] }, { "n": 15, "para": 4, "text": "\"Clerical occupations continue to have the highest exposure levels\", the majority of the 13 occupations in Gradient 4 among them, while exposure rose for \"some strongly digitized occupations\" (the paper; Labor Vector record), financial analysts and web and multimedia developers in Gradient 4 and bank tellers, translators, applications programmers and investment advisers in Gradient 3 (the paper), and for media- and web-related tasks with \"voice, image and video generation\" (ILO brief page); exposure \"declines with tasks of physical nature\" (the paper).", "claims": [ "F31", "F32", "F33", "F34", "F37", "LV-20" ] }, { "n": 16, "para": 4, "text": "Against 2023 the mean score is \"0.29 in 2025 versus 0.30 in 2023\" and the standard deviation \"0.14 in 2025 v. 0.30 in 2023\" (ILO brief page; Labor Vector record), and the authors revise their own 2023 scores for clerical tasks, which \"reflected an overly optimistic assessment of full automation potential\" (the paper).", "claims": [ "F36", "LV-21", "F35" ] }, { "n": 17, "para": 5, "text": "Because \"nearly all occupations have some tasks that require human input\", the paper holds that \"transformation of jobs is the most likely impact of GenAI\" (the paper), the brief that \"most jobs will be transformed rather than made redundant\" (ILO brief page; Labor Vector record, findings), and NASK that the index exists \"not to predict the disappearance of jobs, but to identify where work is most likely to change\" (NASK); the paper qualifies its own reading: \"this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed\" (the paper).", "claims": [ "F40", "F38", "F39", "F42", "LV-22", "F41" ] }, { "n": 18, "para": 5, "text": "Of the class the record places the index in, the employment effects of AI, the archive states that a technology which \"increasingly automates the labor (generation replacing human semantic labor)\" is \"the full vertical integration of semantic production\", its resources \"controlled by the same small set of corporations\" (#499, Rex Fraction), and that labor platforms displace \"does not vanish. It is *relocated*\", the steward capturing it \"as proprietary infrastructure\" (#533, Johannes Sigil).", "claims": [ "O499-01", "O499-02", "O533-01", "O533-02" ] }, { "n": 19, "para": 6, "text": "The paper states what its estimates are not: they \"reflect “exposure” to GenAI and not the actual impact on employment\", represent \"the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented\", leave out \"new jobs that may be created\" and future advances as \"a static snapshot of the exposure of existing occupations to GenAI at the start of 2025\", and do \"not imply the immediate automation of an entire occupation\" (the paper; Labor Vector record, stated limits and coverage), barriers such as \"insufficient digital skills\" and \"the cost of technology\" standing between exposure and adoption (the paper); The People Space carries the limit: \"exposure does not equal automation\" (The People Space).", "claims": [ "F43", "F45", "F46", "LV-23", "LV-24", "LV-15", "F44", "F68" ] }, { "n": 20, "para": 6, "text": "Its further limits, stated by its authors, are that the gradients \"merely suggest the general trends\" because \"any type of task scoring system carries a degree of subjectivity\", that \"all tasks are weighted equally\", that the survey sample \"may not represent the entire working population\" and is comparable only within each 1-digit group, that \"For 91.5% of the tasks, respondents did not report performing a similar task in their work\", that Poland is taken as \"representative of the upper threshold of automation potential\", and that the index \"is generic\" (the paper; Labor Vector record, stated limits).", "claims": [ "F47", "F48", "F49", "F50", "F51", "F52", "LV-25", "LV-26", "LV-27" ] }, { "n": 21, "para": 6, "text": "The record's negative tags for it are \"NOT an observed employment outcome\", \"NOT a forecast of job loss: exposure is an upper threshold of potential\", \"NOT a measure of AI use at work\" and \"the worker survey was run in Poland only\" (Labor Vector record); the Capture Registry finds those limits absent from the composition at the name, whose \"can be heavily impacted\" \"stays inside exposure, and gives no limit\", the index there \"composed at its headline grain\", and holds that the negative tags are \"what separate this resource from the employment trackers beside it in the census\" (Capture Registry, 2026-10-29).", "claims": [ "LV-41", "LV-42", "LV-43", "LV-44", "CAP-1-06", "CAP-1-07", "CAP-1-09", "CAP-1-10" ] }, { "n": 22, "para": 7, "text": "To its organizing question the record answers for this index that its labor object is \"the tasks of occupations\" and \"the global employment in the 112 occupations classed as exposed\", that who supplies the labor is \"not recorded; the index scores tasks' technical feasibility for GenAI automation\", its own scores \"supplied by 1,640 people employed in Poland\" and \"by an expert group\", that who directs it is \"not recorded\", and that gains are \"not recorded as observed outcomes\" (Labor Vector record).", "claims": [ "LV-10", "LV-11", "LV-12", "LV-13" ] }, { "n": 23, "para": 7, "text": "The paper's statement on gains is given \"As argued by numerous economists\": \"the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities\" (the paper), a sentence The People Space quotes as the report's, without \"outright\" and without the economists (The People Space); the paper asks whether jobs can \"retrain and retain existing workers, rather than drive labor churn through workforce replacement\" (the paper), and The People Space names a divide \"in who is most impacted, and who benefits\" (The People Space).", "claims": [ "F53", "F70", "F54", "F69" ] }, { "n": 24, "para": 7, "text": "For the transition the paper asks for social dialogue \"to minimize “technological unemployment”\", holds that \"Workers know their jobs best\", and offers the index, linked to national micro data, as \"a foundation for social dialogue and targeted policy responses to manage the transition\" (the paper).", "claims": [ "F55", "F56", "F57" ] }, { "n": 25, "para": 7, "text": "In the archive's reading, \"The index estimates where task labor could pass to a GenAI system on grounds of technical feasibility, task by task; it meets task-origin only as a prior on which tasks are exposed\", and \"It observes no performed work, so it cannot speak to whether a system's labor advanced a commissioned task, or to user labor drag\" (Labor Vector record, lv_intersection).", "claims": [ "LV-34", "LV-35" ] }, { "n": 26, "para": 7, "text": "The record holds that this evidence answers which occupations have tasks \"scored as technically feasible for GenAI automation\" and what share of employment \"sits in exposed occupations\", and does not answer \"observed employment, wage or hours change in any occupation\", \"whether exposure leads to job loss, transformation or new work\", \"who directs adoption or receives the gains from it\" or \"how GenAI is in fact used at work\"; in its worked question the index is the estimate set beside the observed trackers, since \"An exposure estimate scores what AI could do to a task; an observed outcome counts what happened to jobs\", and \"No record establishes that exposure causes the employment change\" (Labor Vector record, questions).", "claims": [ "LV-28", "LV-29", "LV-30", "LV-31", "LV-32", "LV-33", "LV-48", "LV-49", "LV-50" ] }, { "n": 27, "para": 7, "text": "Of accounts of AI job loss the archive reads one, the Citrini memo's spiral in which \"AI improves, companies cut workers, displaced workers spend less, companies invest more in AI\", as \"a scenario, not a prediction\" narrated backward (#521, Rex Fraction), a crisis that \"has a policy resolution (redistribute gains)\" (#520, Rex Fraction).", "claims": [ "O521-01", "O520-01" ] }, { "n": 28, "para": 8, "text": "Published on 20 May 2025 under DOI 10.54394/HETP0387 and \"Attribution 4.0 International (CC BY 4.0)\", with \"all task-level scores publicly available\" and \"the full dataset (csv)\" on NASK's page (ILO publication page; the paper; NASK; Labor Vector record), copied on Scribd (Scribd card, snippet only), the index lives on the ILO Observatory on AI and Work in the Digital Economy as \"AI impact on jobs\", beside the ILO's platform policy tracker, and the Observatory now lists ILO Working Paper 166 (17 March 2026) and a brief on exposure indicators (17 April 2026), which the record leaves unchecked as to whether they revise this index, as it leaves the task-level scores unchecked as data (ILO Observatory; Labor Vector record).", "claims": [ "F60", "F61", "F58", "F59", "F62", "F63", "F64", "LV-36", "LV-37", "LV-38", "LV-40", "LV-45", "LV-46", "LV-47" ] }, { "n": 29, "para": 8, "text": "A LinkedIn post on the rail has the report presenting \"a comprehensive assessme...\" (LinkedIn card, snippet only), and The People Space gives it \"nearly 30,000 real-world tasks, more than 50,000 human assessments and expert validation\" and glosses its highest gradient as jobs whose tasks \"could feasibly be handled by GenAI without human input\" (The People Space).", "claims": [ "F65", "F66", "F67" ] }, { "n": 30, "para": 8, "text": "Wikipedia has no article on the index (Wikipedia); four articles cite the paper, for a United Nations report predicting that women \"will likely bear the brunt of future layoffs in technological fields\", for Australian firms that \"have cut roles as a result of AI\", for \"the ratio of women in many software fields\" declining, and for US enrolment in computing and STEM degrees dropping \"especially for women\" (Wikipedia: Gender equality in the technology industry; Technological unemployment; Software engineering; Programmer).", "claims": [ "F72", "F73", "F74", "F75", "F76" ] } ], "claims": { "F1": { "kind": "field", "quote": "This study updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI), incorporating recent advances in the technology and increasing user familiarity with GenAI tools.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶1 s1", "modality": "stipulation" }, "F2": { "kind": "field", "quote": "This ILO Working Paper refines the global measurement of occupational exposure to generative AI by combining task-level data, expert input, and AI model predictions.", "source": "B1", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization", "locus": "the publication page, lede", "modality": "stipulation" }, "LV-01": { "kind": "archive", "quote": "Generative AI and Jobs: A Refined Global 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Research Institute of the Ministry of Digital Affairs in Poland, to develop a more precise assessment of potential effects of GenAI on employment across countries.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Introduction ¶3", "modality": "documented" }, "F6": { "kind": "field", "quote": "Authors Who prepared the report? NASK Analysis and Research Office … it played an active role in the preparation of this report.", "source": "B4", "source_label": "NASK – Państwowy Instytut Badawczy — ILO-NASK Indeks", "locus": "'Authors Who prepared the report?'", "modality": "documented" }, "LV-04": { "kind": "archive", "quote": "International Labour Organization (ILO), with NASK and the Polish Ministry of Family, Labour and Social Policy (expert group); authors Paweł Gmyrek, Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Balint Nafradi, Konrad Rosłaniec, Marek Troszyński", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].operator", "modality": "documented", "date": "2026-10-29" }, "F7": { "kind": "field", "quote": "Author(s) Pawel Gmyrek Janine Berg Karol Kamiński Filip Konopczyński Agnieszka Ładna Balint Nafradi Konrad Rosłaniec Marek Troszyński", "source": "B1", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure | International Labour Organization", "locus": "'Additional details', Author(s)", "modality": "documented" }, "F8": { "kind": "field", "quote": "Paweł Gmyrek Janine Berg Karol Kamiński Filip Konopczyński Agnieszka Ładna Konrad Rosłaniec Marek Troszyński Abstract", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "title block", "modality": "documented" }, "F9": { "kind": "field", "quote": "This study builds on the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter) , which used task descriptions from the 4-digit International Standard Classification of Occupations (ISCO-08) and the GPT-4 model to estimate task-level and occupational scores of exposure to GenAI technology", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Introduction ¶4", "modality": "documented" }, "F10": { "kind": "field", "quote": "Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.", "source": "B6", "source_label": "YouTube·International Labour Organization — Generative AI and Jobs: A global analysis of potential effects on job quantity and quality.", "locus": "card title (video, 11m; unreachable: no URL found)", "modality": "documented" }, "LV-39": { "kind": "archive", "quote": "2023 (the first ILO index, Gmyrek, Berg and Bescond); this refinement 2025-05-20", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].founded", "modality": "documented", "date": "2026-10-29" }, "F13": { "kind": "field", "quote": "relying on three advanced LLMs available on the market at the end of 2024: GPT-4, GPT-4o and Gemini Flash 1.5.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.1 Initial algorithmic predictions", "modality": "documented" }, "F14": { "kind": "field", "quote": "a score ranging from 0 to 1 for each individual task, where 0 means the task cannot be automated and 1 means the task could be fully automated with GenAI technology (the task can be conducted without participation of a human operator).", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.1 Initial algorithmic predictions", "modality": "stipulation" }, "F11": { "kind": "field", "quote": "Using a representative sample from the 29,753 tasks in the Polish occupational classification system and a survey of 1,640 people employed in each 1-digit ISCO-08 groups, we collect 52,558 data points regarding perceive potential of automation for 2,861 tasks.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶1 s2", "modality": "documented" }, "F12": { "kind": "field", "quote": "This means that tasks of managers are assessed by individuals working as managers, tasks of professionals are assessed by professionals, etc.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.3 Survey design and recruitment", "modality": "stipulation" }, "LV-16": { "kind": "archive", "quote": "29,753 Polish 6-digit tasks scored; 3,265 ISCO-08 tasks rescored; 1,640 survey respondents; 52,558 data points; 112 occupations classed as exposed", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].count.value", "modality": "documented", "date": "2026-10-29" }, "CAP-1-05": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, findings[2]", "modality": "documented", "date": "2026-10-29" }, "F15": { "kind": "field", "quote": "We then compare this input with a survey and several rounds of Delphi-style discussions among a smaller group of international experts.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶1 s3", "modality": "documented" }, "F16": { "kind": "field", "quote": "While the experts are expected to have familiarity with the current family of generative AI technologies and their applications, expertise in AI design or development is not a requirement.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§4 Expert validation survey ¶1", "modality": "stipulation" }, "F17": { "kind": "field", "quote": "Based on this process, we create a repository of knowledge about task automation that goes beyond national specificities and use it to develop an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶1 s4", "modality": "documented" }, "F18": { "kind": "field", "quote": "the correlation between predictions based on ISCO-08 tasks and Polish 6-digit tasks is 0.92, demonstrating exceptional stability of predictions.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§8 Prediction of synthetic task-level scores", "modality": "self-description" }, "LV-14": { "kind": "archive", "quote": "LLM-generated task scores (GPT-4, GPT-4o, Gemini Flash 1.5), a survey of workers scoring a representative task sample (52,558 data points on 2,861 tasks), expert validation with Delphi-style rounds, then an AI predictor fed the human scores to score all tasks; employment shares from the ILO's global estimation model on harmonised labour force microdata", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].method", "modality": "documented", "date": "2026-10-29" }, "F19": { "kind": "field", "quote": "we rely on two moments of distribution: occupational mean and standard deviation (SD) of task-level scores pertaining to a given 4-digit occupation.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§9 Adjusted Global Index ¶1", "modality": "stipulation" }, "F20": { "kind": "field", "quote": "Gradient 4 highlights occupations with the highest share of tasks exposed to potential GenAI-driven automation, and with a high consistency of this exposure across tasks (Highest exposure, low task variability).", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§9 ¶3 (the gradients)", "modality": "stipulation" }, "F21": { "kind": "field", "quote": "Defines four progressively increasing gradients of GenAI exposure depending on the mean exposure score and the degree of task variability for each ISCO-08 occupation.", "source": "B2", "source_label": "International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "locus": "the brief, bullet 3", "modality": "stipulation" }, "F22": { "kind": "field", "quote": "we focus on the 112 occupations identified as exposed and estimate the share of employment that these occupations represent at the global, regional, and income-based levels.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 Revised global employment estimates ¶1", "modality": "documented" }, "LV-09": { "kind": "archive", "quote": "a task's automation score, aggregated to a 4-digit ISCO-08 occupation (mean and standard deviation) and placed in one of four exposure gradients; employment shares then estimated by country income group, region and sex", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].unit", "modality": "stipulation", "date": "2026-10-29" }, "F23": { "kind": "field", "quote": "Globally, one in four workers are in an occupation with some GenAI exposure. 3.3% of global employment falls into the highest exposure category, albeit with significant differences between female (4.7%) and male employment (2.4%).", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶3 s1–2", "modality": "documented" }, "LV-17": { "kind": "archive", "quote": "one in four workers globally (24%) is in an occupation with some GenAI exposure", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[0]", "modality": "documented", "date": "2026-10-29" }, "LV-18": { "kind": "archive", "quote": "3.3% of global employment falls in the highest exposure gradient (4.7% of female, 2.4% of male employment); in high-income countries 9.6% female and 3.5% male", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[1]", "modality": "documented", "date": "2026-10-29" }, "CAP-1-01": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, findings[0] s1–2", "modality": "documented", "date": "2026-10-29" }, "CAP-1-02": { "kind": "archive", "quote": "Checked 2026-10-29 against the Labor Vector census record of the same day and the sources it saved.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, findings[0] s3", "modality": "documented", "date": "2026-10-29" }, "CAP-1-08": { "kind": "archive", "quote": "The innocent reading first. The Overview names the paper and gives its headline figures and method, from the ILO's pages and commentary.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, reading", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-11": { "kind": "archive", "quote": "A baseline: the record landed on laborvector.org the same day (commit bf90841) and no card cites it. NEW address.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, q_kind s2–4", "modality": "documented", "date": "2026-10-29" }, "F24": { "kind": "field", "quote": "These differences increase with countries’ income (9.6% female vs 3.5% male in Gradient 4 in HICs), and so does the overall exposure (11% of total employment in LICs vs 34% in HICs).", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶3 s3", "modality": "documented" }, "F25": { "kind": "field", "quote": "However, when disaggregated by gender, 28% of male employment is exposed, with only 3.5% in gradient 4, compared to 41% of female employment, with 14.4% in gradient 3 and 9.6% in gradient 4.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶2", "modality": "documented" }, "F26": { "kind": "field", "quote": "Gender disparities are also more pronounced in wealthier regions, particularly in Europe and Central Asia, where 39% of female employment falls into one of the four exposure gradients compared to 26% for men.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶3", "modality": "documented" }, "LV-19": { "kind": "archive", "quote": "exposed employment is 34% in high-income countries and 11% in low-income countries", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[2]", "modality": "documented", "date": "2026-10-29" }, "F27": { "kind": "field", "quote": "These patterns reflect both occupational structures and the extent to which GenAI-exposed roles, such as clerical, financial, and customer service jobs, are concentrated in higher-income countries.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶3", "modality": "interpretation" }, "F28": { "kind": "field", "quote": "This reflects the gendered distribution of clerical and customer-facing roles, which are both more exposed and more common among female workers.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'Exposure is gendered and worse in wealthier economies' ¶2", "modality": "interpretation" }, "F29": { "kind": "field", "quote": "This difference is shaped by infrastructure, digital access and workforce structure.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'Location matters. So does income.' ¶2", "modality": "interpretation" }, "F71": { "kind": "field", "quote": "How is generative AI reshaping gender inequalities at work?", "source": "B7", "source_label": "YouTube·International Labour Organization — How is generative AI reshaping gender inequalities at work?", "locus": "card title (video, 3m; unreachable: no URL found)", "modality": "documented" }, "CAP-1-03": { "kind": "archive", "quote": "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.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, findings[1] s1–2", "modality": "documented", "date": "2026-10-29" }, "CAP-1-04": { "kind": "archive", "quote": "'roughly 29% vs. 16% across sampled countries' is in no saved source; the paper's 29.2% is the 2023 index's combined share of augmentation and 'the big unknown'.", "deposit": null, "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry ilo-generative-ai-and-jobs-index-aio-20261029 (OBS-79a0cee7426a, AXN:0AEF.CAPTURE.🔎♋🔀✋⏏️🌠)", "record": "https://www.alexanarch.org/captures/ilo-generative-ai-and-jobs-index-aio-20261029/", "locus": "ilo-generative-ai-and-jobs-index-aio-20261029, findings[1] s3", "modality": "documented", "date": "2026-10-29" }, "F30": { "kind": "field", "quote": "In addition, the 2023 estimate attributed 13% of employment as subject to “augmentation” and an additional 16.2% of employment in “the big unknown”. The combined employment share for Gradients 1-3 for the world in the current analysis is 20.5%, 9 percentage points lower than combined shares of augmentation and “the big unknown” (29.2%) reported in 2023.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶4", "modality": "documented" }, "F31": { "kind": "field", "quote": "Clerical occupations continue to have the highest exposure levels. Additionally, some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶2 s2–3", "modality": "documented" }, "F32": { "kind": "field", "quote": "Among the 13 occupations classified in gradient 4, the majority represent clerical roles, including data entry clerks, typists and word processing operators, accounting and bookkeeping clerks, and general office clerks.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§10 Changes to occupational classifications ¶2", "modality": "documented" }, "F33": { "kind": "field", "quote": "some occupations – such as financial analysts, and web and multimedia developers (gradient 4), as well as bank tellers, translators, applications programmers, investment advisers (gradient 3) – have increased their levels of exposure", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Conclusion ¶5", "modality": "documented" }, "F34": { "kind": "field", "quote": "the highest exposure to GenAI is typically observed among groups with the largest share of tasks of cognitive nature and it declines with tasks of physical nature, which are more prominent among occupational groups 4-9 (Figure 2).", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.1 Initial algorithmic predictions ¶2", "modality": "documented" }, "F37": { "kind": "field", "quote": "Growing abilities of GenAI models in such areas as voice, image and video generation have increased automation scores for a range of tasks in media- and web-related occupations.", "source": "B2", "source_label": "International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "locus": "the brief, bullet 5", "modality": "documented" }, "LV-20": { "kind": "archive", "quote": "clerical occupations continue to have the highest exposure; some digitised professional and technical occupations increased", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[3]", "modality": "documented", "date": "2026-10-29" }, "F36": { "kind": "field", "quote": "Overall, the automation scores are slightly lower than in 2023 (a mean automation score of 0.29 in 2025 versus 0.30 in 2023), though the variability of scores is considerably lower (standard deviation 0.14 in 2025 v. 0.30 in 2023).", "source": "B2", "source_label": "International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "locus": "the brief, bullet 4", "modality": "documented" }, "LV-21": { "kind": "archive", "quote": "mean automation score 0.29 in 2025 against 0.30 in 2023, with standard deviation 0.14 against 0.30", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[4]", "modality": "documented", "date": "2026-10-29" }, "F35": { "kind": "field", "quote": "our 2023 scores for such tasks, which reached 0.9 in some cases, reflected an overly optimistic assessment of full automation potential. In reality, many of these tasks still require substantial human effort.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§10 ¶2", "modality": "self-description" }, "F40": { "kind": "field", "quote": "Indeed, the results indicate that few jobs consist of tasks that are fully automatable with current GenAI technology; nearly all occupations have some tasks that require human input.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶6", "modality": "documented" }, "F38": { "kind": "field", "quote": "As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Abstract ¶3 s4", "modality": "interpretation" }, "F39": { "kind": "field", "quote": "One in four workers across the world are in an occupation with some degree of GenAI exposure, but because of the continued need for human input, most jobs will be transformed rather than made redundant.", "source": "B2", "source_label": "International Labour Organization — Generative AI and jobs: A 2025 update | International Labour Organization", "locus": "the brief, bullet 6", "modality": "interpretation" }, "F42": { "kind": "field", "quote": "The purpose of the ILO–NASK Index is not to predict the disappearance of jobs, but to identify where work is most likely to change.", "source": "B4", "source_label": "NASK – Państwowy Instytut Badawczy — ILO-NASK Indeks", "locus": "'How We Built the ILO–NASK Index' ¶4", "modality": "stipulation" }, "LV-22": { "kind": "archive", "quote": "most jobs are more likely to be transformed than made redundant, because of the continued need for human input", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].findings[5]", "modality": "interpretation", "date": "2026-10-29" }, "F41": { "kind": "field", "quote": "However, this does not mean that demand for these occupations will remain stable, as the efficiencies gained from introducing technology may mean that fewer workers are needed.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶6", "modality": "hypothesis" }, "O499-01": { "kind": "archive", "quote": "Artificial intelligence represents the full vertical integration of semantic production — a single technology that owns the instruments (compute), processes the objects (training data), and increasingly automates the labor (generation replacing human semantic labor).", "deposit": 499, "source_label": "#499 AUTONOMOUS SEMANTIC WARFARE A Field Manual for Meaning in the Age of Platform Capture", "record": "https://www.alexanarch.org/s/records/499/", "locus": "§2.5 'AI and the Future of Semantic Production' ¶1", "modality": "interpretation", "date": "2026-02-20" }, "O499-02": { "kind": "archive", "quote": "All three resources are controlled by the same small set of corporations that dominate semantic infrastructure generally.", "deposit": 499, "source_label": "#499 AUTONOMOUS SEMANTIC WARFARE A Field Manual for Meaning in the Age of Platform Capture", "record": "https://www.alexanarch.org/s/records/499/", "locus": "§2.5 ¶4", "modality": "interpretation", "date": "2026-02-20" }, "O533-01": { "kind": "archive", "quote": "He correctly notes that platforms displace this labor. He does not note that the labor does not vanish. It is *relocated* — from visible human interpreters to invisible architectural constraints.", "deposit": 533, "source_label": "#533 The Groundskeeper's Treatise: A Diagnostic Reading of Platform Stewardship Theory through the Semantic Economy", "record": "https://www.alexanarch.org/s/records/533/", "locus": "'Labor bearing-cost' ¶1", "modality": "interpretation", "date": "2026-03-04" }, "O533-02": { "kind": "archive", "quote": "What was distributed human judgment becomes centralized architectural authority. The steward captures the labor of reconciliation as proprietary infrastructure. This is the *enclosure of interpretive labor*.", "deposit": 533, "source_label": "#533 The Groundskeeper's Treatise: A Diagnostic Reading of Platform Stewardship Theory through the Semantic Economy", "record": "https://www.alexanarch.org/s/records/533/", "locus": "'Labor bearing-cost' ¶1", "modality": "interpretation", "date": "2026-03-04" }, "F43": { "kind": "field", "quote": "It is important to stress that these new estimates reflect “exposure” to GenAI and not the actual impact on employment. As in 2023, the 2025 estimates represent the upper threshold of the percentage of employment that could be affected if GenAI technology were fully implemented.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶5", "modality": "self-description" }, "F45": { "kind": "field", "quote": "Finally, the estimates do not account for new jobs that may be created, nor the technological advances that could potentially automate more tasks in the future. In that sense, our estimates present a static snapshot of the exposure of existing occupations to GenAI at the start of 2025.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶7", "modality": "self-description" }, "F46": { "kind": "field", "quote": "we stress that such exposure does not imply the immediate automation of an entire occupation, but rather the potential for a large share of its current tasks to be performed using this technology.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Conclusion ¶7", "modality": "self-description" }, "LV-23": { "kind": "archive", "quote": "the estimates measure exposure and give an upper threshold; they are not the actual impact on employment", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].stated_limits[0]", "modality": "self-description", "date": "2026-10-29" }, "LV-24": { "kind": "archive", "quote": "they do not account for new jobs created or future technical advances: a static snapshot at the start of 2025", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].stated_limits[1]", "modality": "self-description", "date": "2026-10-29" }, "LV-15": { "kind": "archive", "quote": "a static snapshot of exposure at the start of 2025; published 20 May 2025", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].coverage.period", "modality": "documented", "date": "2026-10-29" }, "F44": { "kind": "field", "quote": "Infrastructure constraints (electricity, broadband), insufficient digital skills, the cost of technology, and inherent operational difficulties associated with the technology are just some of the barriers to full adoption.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§11 ¶5", "modality": "interpretation" }, "F68": { "kind": "field", "quote": "A critical distinction in the ILO’s analysis is that exposure does not equal automation. These are best-case, upper-limit scenarios – estimates of what could be done with GenAI, not what will be.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'Caution, not panic: exposure is not impact' ¶1", "modality": "interpretation" }, "F47": { "kind": "field", "quote": "We stress that these classifications merely suggest the general trends and types of occupations at different levels of exposure, since any type of task scoring system carries a degree of subjectivity.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§9 ¶5", "modality": "self-description" }, "F48": { "kind": "field", "quote": "One of the limitations of the current approach is that neither ISCO-08 nor the Polish classification system includes information on the relative frequency or importance of tasks within occupations. As a result, all tasks are weighted equally when calculating exposure scores.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "footnote 18", "modality": "self-description" }, "F49": { "kind": "field", "quote": "On the downside, this survey design means that the sample may not represent the entire working population, and interpretability of opinions on the automation potential is limited to comparisons within each 1-digit group.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.4 Survey design limitations", "modality": "self-description" }, "F50": { "kind": "field", "quote": "For 91.5% of the tasks, respondents did not report performing a similar task in their work", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§3.5 ¶8", "modality": "documented" }, "F51": { "kind": "field", "quote": "it seems reasonable to assume that Poland is representative of the upper threshold of automation potential, due to high availability of digital equipment and widespread internet access.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§2 Tasks and occupations in the 6-digit system in Poland ¶1", "modality": "interpretation" }, "F52": { "kind": "field", "quote": "The new global index presented in this paper is generic, due to the nature of ISCO-08 and the structure of tasks associated with occupations in this framework.", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Conclusion ¶3", "modality": "self-description" }, "LV-25": { "kind": "archive", "quote": "the survey sample may not represent the entire working population; 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Its scores were supplied by 1,640 people employed in Poland, each scoring tasks of their own 1-digit occupational group, and by an expert group", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].direction.supplies", "modality": "interpretation", "date": "2026-10-29" }, "LV-12": { "kind": "archive", "quote": "not recorded", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].direction.directs", "modality": "interpretation", "date": "2026-10-29" }, "LV-13": { "kind": "archive", "quote": "not recorded as observed outcomes; it estimates employment shares exposed, by sex and income group, and states that the productivity benefits depend on complementing human expertise", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Generative AI and Jobs: A Refined Global Index of Occupational Exposure (https://www.laborvector.org/aggregators/ilo-genai-exposure-index/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index/", "locus": "census.json rows[slug == 'ilo-genai-exposure-index'].direction.gains", "modality": "interpretation", "date": "2026-10-29" }, "F53": { "kind": "field", "quote": "As argued by numerous economists, the key to unlocking the productivity benefits of GenAI lies not in the search of outright labour savings, but in the extent to which human expertise can be complemented with new technological capacities to reshape occupations and generate additional value-added", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "Conclusion ¶7", "modality": "attributed" }, "F70": { "kind": "field", "quote": "The key to unlocking the productivity benefits of GenAI lies not in the search for labour savings, but in the extent to which human expertise can be complemented by new technological capacities.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'What next?' ¶2", "modality": "attributed" }, "F54": { "kind": "field", "quote": "A key question – largely dependent on the policies shaping this evolution – is to what extent these jobs can retrain and retain existing workers, rather than drive labor churn through workforce replacement and the recruitment of typically younger specialists", "source": "B9", "source_label": "International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140, HTML edition)", "locus": "§10 ¶4", "modality": "interpretation" }, "F69": { "kind": "field", "quote": "But it also underscores the risk of a growing global divide in who is most impacted, and who benefits, from GenAI.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'Location matters. 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Thomson — A Refined Global Index of Occupational Exposure | Grace S. Thomson", "locus": "card snippet (LinkedIn unreachable: no URL found)", "modality": "interpretation" }, "F66": { "kind": "field", "quote": "Drawing on nearly 30,000 real-world tasks, more than 50,000 human assessments and expert validation it presents a refined global index of occupational exposure to generative AI.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "¶1", "modality": "interpretation" }, "F67": { "kind": "field", "quote": "Just 3.3% of jobs fall into the highest exposure category – those where most tasks could feasibly be handled by GenAI without human input.", "source": "B5", "source_label": "The People Space — What ILO's Global Index tells us about GenAI and jobs | The People Space", "locus": "'One in four jobs touched by GenAI' ¶1", "modality": "interpretation" }, "F72": { "kind": "field", "quote": "Wikipedia does not have an article with this exact name.", "source": "B11", "source_label": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)", "locus": "Wikipedia: titles 'Generative AI and Jobs: A Refined Global Index of Occupational Exposure', 'Global Index of Occupational Exposure to Generative AI', 'ILO Generative AI and Jobs index'", "modality": "documented" }, "F73": { "kind": "field", "quote": "A [[United Nations]] report predicts that females will likely bear the brunt of future layoffs in technological fields.", "source": "B11", "source_label": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)", "locus": "Gender equality in the technology industry, §Statistics (cites WP140)", "modality": "interpretation" }, "F74": { "kind": "field", "quote": "In Australia 49% of larger firms and 37% of Small and Medium Enterprises have cut roles as a result of AI.", "source": "B11", "source_label": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)", "locus": "Technological unemployment, §Artificial intelligence (cites WP140 beside Yahoo Lifestyle)", "modality": "documented" }, "F75": { "kind": "field", "quote": "Furthermore, the ratio of women in many software fields has also been declining over the years as compared to other engineering fields.", "source": "B11", "source_label": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)", "locus": "Software engineering, §Employment, United States (cites WP140 beside an HP blog)", "modality": "documented" }, "F76": { "kind": "field", "quote": "enrollment in computer-related degrees and other STEM degrees (STEM attrition) … in the US has been dropping for years, especially for women,", "source": "B11", "source_label": "Wikipedia — Generative AI and Jobs: A Refined Global Index of Occupational Exposure (no article; cited in four articles)", "locus": "Programmer, §Market changes in the US (cites WP140 beside an HP blog)", "modality": "documented" } }, "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority." }, "source": "operator, 2026-10-29: 'lets expand the research, implement the same meta aggregator function with pages... then /non entries after i get the captures'; the Labor Vector meta-aggregator entity (laborvector.org/aggregators/ilo-genai-exposure-index/)", "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; checked by the session and the page builder)", "date": "2026-10-29", "status": { "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ρ = extends, read from the Labor Vector census record, the capture record and the deposits that bear on the index's class (#499, #520, #521, #533; the terms of the record's lv_intersection read in #771); archive selected by D/R/O (no D or R candidate: no archive text names the index; 5 deposits admitted by reading at O-class, 4 with claims and 1 grouped as an instance; hop of 47 read and rejected), with the Labor Vector census record (50 claims, with the worked question) and the Capture Registry's findings, reading, analysis and query note (11 claims) ledgered as the archive's records of the entity; the field B by three origins (8 cards, 4 of them unreachable or title-only; 2 own pages; Wikipedia, no article, four citing articles); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 76/76 verified by script against saved text, raw wikitext or card text; archive quotes 67/67 verified against the registry, census.json, questions.json and the deposit texts", "frozen": false, "breaches": [] }, "addresses": [ { "address": "ILO Generative AI and Jobs index", "address_url": "https://www.alexanarch.org/addresses/ilo-generative-ai-and-jobs-index/", "register": [] } ], "counts": { "ko_sentences": 30, "p_words": 346, "field_claims": 76, "archive_claims": 67, "ko_field_claims_cited": 76, "ko_archive_claims_cited": 67, "admitted_deposits": 5, "register_addresses": 0, "traversal": { "D_sentences": 0, "D_deposits": 0, "R_sentences": 0, "R_deposits": 0, "O_direct": 0, "O_class": 3, "O_deposits": 3, "H_candidates": 47 } }, "links": { "record": "https://www.alexanarch.org/non/ilo-genai-exposure-index/", "table_of_contents": "https://www.alexanarch.org/non/index.json", "register": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/register.json", "row": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/rows/ilo-genai-exposure-index.json", "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/ilo-genai-exposure-index/ledger-archive.json", "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/ilo-genai-exposure-index/field.json", "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ilo-genai-exposure-index/", "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ilo-genai-exposure-index/", "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ilo-genai-exposure-index/summary.json", "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/ilo-genai-exposure-index/reading.json", "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/ilo-genai-exposure-index.json", "address_page": "https://www.alexanarch.org/addresses/ilo-generative-ai-and-jobs-index/", "labor_record": "https://www.laborvector.org/aggregators/ilo-genai-exposure-index", "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative" }, "row_sha256": "e44204a15b0d959c04e8dfcb8af4c9ea6a5570eda61bdfc1e26c83181f6a7fce", "record_url": "https://www.alexanarch.org/non/ilo-genai-exposure-index/", "json_url": "https://www.alexanarch.org/non/ilo-genai-exposure-index/entity.json", "spec": "https://www.alexanarch.org/s/records/1690/", "creator": "Lee Sharks", "orcid": "0009-0000-1599-0703", "license": "CC BY 4.0" }