Each entry gives the study, how it was done, what it found, what it supports and, as plainly, what it does not. Grades describe what kind of evidence a source is; how to read them is on the main page. Every entry has a permanent link and a copyable citation.
Institutional modelling#
Digitaliseringsstyrelsen (Danish Agency for Digital Government) (2018). Vejledning om digitaliseringsklar lovgivning
VEJ nr 9590 af 12/07/2018, mandatory for government bills since 1 July 2018, status Gaeldende as at September 2026
- Method
- Binding guidance on the drafting of Danish government legislation. Every government bill is assessed against seven principles before it goes to Parliament.
- Finding
- The third principle carries a written checklist question: 'Er det sikret, at det fagprofessionelle skon er opretholdt i tilfaelde, hvor hensynet til borgernes retssikkerhed taler herfor?' (Has it been ensured that professional discretion has been preserved in cases where regard for citizens legal certainty so requires?) The same document states that objective rules should be used only where it makes sense and where professional discretion is not needed, and places a residual duty on the authority to ensure discretion continues to be exercised. The 2018 wording is advisory; the Ministry of Justice s current legislative-quality guidance states that new legislation shall be digital-ready.
- What it supports
- That a state has built a compulsory written checkpoint on whether human judgement survives a change of process, answered by a named official before a parliamentary vote. It is a procedural instrument for preserving discretion rather than a rule about technology.
- What it does not support
- That discretion is in fact preserved. It measures a drafting process, not outcomes, no published review of how the question is answered was found, and it binds those who draft legislation rather than those who decide individual cases.
Statutory investigation#
Deputy Parliamentary Ombudsman of Finland (Maija Sakslin) (2019). Decision on the Tax Administration s automated decision making
EOAK/3379/2018, decision of 20 November 2019
- Method
- Own-initiative investigation by the Parliamentary Ombudsman into the legality of automated decision making at the Finnish Tax Administration, which issued on the order of fifteen million decisions a year.
- Finding
- The Ombudsman found the practice unlawful. The reasoning turns on accountability rather than error: official accountability had become indirect (virkavastuu jaa valilliseksi), because no identifiable official could be said to have made the decision. Parliament subsequently legislated chapter 8 b of the Administrative Procedure Act, in force 2023, and the Chancellor of Justice applied the new law against Kela in April 2025.
- What it supports
- That a supervisory body identified the loss of an answerable human as the defect, independent of whether the decisions were correct, and that a legislature acted on that finding. Four bodies over six years reaching the same conclusion about the same problem is an unusually complete chain.
- What it does not support
- That any decision was wrong. The finding is about accountability structure, not accuracy, and the Ombudsman did not measure outcomes. It concerns Finnish administrative law and does not transfer to private-sector decisions.
Institutional modelling#
National Assembly of Quebec (2021). Act respecting the protection of personal information in the private sector, s 12.1
CQLR c P-39.1 s 12.1, inserted by SQ 2021 c 25 s 110, in force 22 September 2023. Public-sector mirror at CQLR c A-2.1 s 65.2 (SQ 2021 c 25 s 21). Consolidation current to 7 April 2026
- Method
- Provincial statute, read at the official consolidated text in both official languages. One version only, unamended since coming into force.
- Finding
- Where an enterprise uses personal information to render a decision based exclusively on automated processing, it must say so by the time it communicates the decision, and on request give the information used, the reasons and principal factors, and the right of correction. It then requires that 'the person concerned must be given the opportunity to submit observations to a member of the personnel of the enterprise who is in a position to review the decision'. The French is impersonal: 'Il doit etre donne a la personne concernee l occasion de presenter ses observations a un membre du personnel de l entreprise en mesure de reviser la decision.'
- What it supports
- That a jurisdiction has legislated not a right to an explanation but a right to put arguments to a named human with authority to change the answer. It is the strongest human-review provision found in North America and it names a capacity, being in a position to review, rather than a role.
- What it does not support
- That it is used, or that the reviewing person is competent to redo the analysis. The statute does not define what being in a position to review requires. Quebec s own regulator lists four limits, graded separately below. It applies only to decisions based EXCLUSIVELY on automated processing, which excludes most decisions in practice.
Institutional modelling#
Korea Development Institute (KDI) (2023). Changes in the labour market due to artificial intelligence and policy directions (Research Report 2023-03)
KDI, published in Korean
- Method
- Expert and GPT-4 capability ratings applied to Korean occupational profiles, a KDI survey of 800 firms in September 2023, and firm-panel econometrics.
- Finding
- 38.8 percent of jobs are technically automatable across more than 70 percent of their tasks, yet only 2.7 percent of firms with ten or more staff had adopted AI. Realised effects showed no aggregate employment change, lower earnings, and the impact concentrated on YOUNGER, tertiary-educated workers and women.
- What it supports
- That the gap between technical potential and actual adoption is enormous, and that where effects appear they fall on the young and educated rather than the low-skilled.
- What it does not support
- That the Korean pattern transfers. Korea has unusually high tertiary education rates and a distinctive labour market.
Institutional modelling#
Parliament of Finland (2023). Administrative Procedure Act, chapter 8 b, automated decision making
Hallintolaki ss 53 e to 53 g, inserted by act 487/2023 of 23 March 2023, in force 1 May 2023, with companion act 488/2023
- Method
- General administrative statute, not an AI-specific instrument. Applies to all automated decision making by Finnish authorities.
- Finding
- An authority may decide a matter automatically only where the matter 'contains no elements requiring case-by-case discretion' (johon ei sisally seikkoja, jotka edellyttavat tapauskohtaista harkintaa). A decision counts as automated where it is reached without a natural person checking and approving it. Section 53 e(4) provides that a request for rectification may not be decided automatically. The companion act requires a named individual responsible for each system, a published deployment decision, and records permitting five-year reconstruction of the stages at which a natural person took part.
- What it supports
- That the human-discretion test can be stated in a single clause of general administrative law, and that a legislature has done so. It defines what makes a matter unsuitable for automation, which most AI frameworks require oversight without ever specifying.
- What it does not support
- That it works, or that Finnish authorities classify matters correctly. It binds public authorities only, not private employers. Finland s implementation of the EU AI Act is separately late, so this is administrative-law strength rather than AI-law strength.
Institutional survey#
Saudi Data and AI Authority (SDAIA) (2023). AI Ethics Principles, Version 1.0
SDAIA, September 2023
- Method
- National AI ethics guidance, seven principles with an assessment checklist annexe covering the AI system lifecycle. Non-binding guidance rather than statute. Version 1.0 read in full at source; whether a later version exists could not be checked because SDAIA's main host rejects automated requests.
- Finding
- The checklist annexe puts this question to designers at the plan and design stage: "Does your AI system design prevent overconfidence in or overreliance on the AI system with necessary human intervention mechanisms?" It also asks whether human oversight processes carry defined KPIs and assigned responsibility. The operative text states that decisions which are irreversible or life-and-death "should trigger human oversight and final determination", and rules out social scoring and mass surveillance.
- What it supports
- That over-reliance on AI has been named as a design defect to be engineered against in a national governance instrument. On the evidence gathered here it is the only Gulf instrument that does so.
- What it does not support
- Any obligation. It is guidance, not law, the over-reliance language sits in a checklist annexe rather than in the principle text, and Saudi Arabia had no binding AI statute as of August 2026, only a draft responsible AI policy out for consultation from 2 April 2026. Version 1.0 dates from September 2023 and may have been superseded.
Institutional survey#
Government of the United Arab Emirates (2024). The UAE Charter for the Development and Use of Artificial Intelligence
UAE Legislation portal, issued 10 June 2024
- Method
- National charter of twelve principles. Statement of principle with no duty-holder, no enforcement mechanism and no competence requirement.
- Finding
- Principle 6, Human Oversight, "emphasizes the irreplaceable value of human judgment and human oversight over AI, aligning with ethical values and social standards to correct any errors or biases that may arise". The charter is silent on deskilling, over-reliance and any obligation to train or assess the humans doing the overseeing.
- What it supports
- That human oversight is stated as a national principle in the UAE.
- What it does not support
- That it is operational. There is no named duty-holder, no enforcement, no competence standard and no test of whether oversight is real. The UAE had no federal AI statute as of August 2026.
Institutional modelling#
Institut fur Arbeitsmarkt- und Berufsforschung (IAB), Germany (2024). Folgen des technologischen Wandels fur den Arbeitsmarkt (Consequences of technological change for the labour market: it is above all the highly qualified who feel digitalisation)
IAB-Kurzbericht 5/2024, published in German
- Method
- The Substituierbarkeitspotenziale series, 2022 wave. Three independent coders score more than 9,000 tasks in the BERUFENET expert database across roughly 4,600 occupations.
- Finding
- Substitutability rose about ten percentage points for degree-level expert occupations between 2019 and 2022, and was roughly flat for helper occupations. IAB frames AI as relief for skills shortages rather than as displacement.
- What it supports
- That the German expert assessment puts the pressure on the highly qualified, which inverts the assumption that automation threatens the least skilled first.
- What it does not support
- Realised outcomes. Substitutability is technical potential assessed by coders, not what employers did.
Institutional survey#
LaborIA (French Ministry of Labour, Inria and Matrice) (2024). Etude des impacts de l'IA sur le travail: Rapport d'enquete LaborIA Explorer (Study of the impacts of AI on work)
LaborIA, published in French
- Method
- Telephone survey of 250 decision-makers in firms with more than 50 staff (42 with AI deployed), longitudinal interviews with 10 decision-makers across three waves, and six ethnographic field sites.
- Finding
- Names a conflit de rationalite, a clash of rationalities: managers justify AI by error reduction (81 percent), performance (75 percent) and removing drudgery (74 percent), while fieldwork shows workers becoming the system's de facto trainers.
- What it supports
- That what management believes AI is doing and what workers experience it doing can diverge systematically inside the same organisation. A work-psychology and ergonomics frame rather than task-exposure modelling.
- What it does not support
- Scale. The qualitative core rests on six sites and ten repeated interviews.
Institutional survey#
BAuA, ZEW, IAB and BIBB, Germany (2025). Digitalisierung und Wandel der Beschaftigung, DiWaBe 2.0 (Digitalisation and the transformation of employment)
Bundesanstalt fur Arbeitsschutz und Arbeitsmedizin, published in German
- Method
- Representative 2024 survey of roughly 9,800 employees subject to social insurance, linkable to administrative employer and employee records.
- Finding
- More than half already use AI at work but largely informally. Use ranges from about a third of unqualified workers to around 80 percent of those with a degree or Meister qualification. There was NO difference in training participation between AI users and non-users.
- What it supports
- That AI use is spreading through workplaces without any corresponding increase in training, which is the adoption-without-redesign pattern measured directly at national scale.
- What it does not support
- What that absence of training does to capability over time. The survey is a single 2024 snapshot.
Institutional survey#
Bundesinstitut fuer Berufsbildung (2025). Angespannte Lage auf dem Ausbildungsmarkt (Tense situation in the training market)
BIBB press release 40/2025, 10 December 2025
- Method
- Official German statistics on the dual vocational training system, counting newly concluded training contracts, unfilled training places and unplaced applicants as at 30 September 2025.
- Finding
- Around 476,000 new dual training contracts in 2025, down 2.1 per cent on 2024 and the second consecutive annual fall. 54,400 training places went unfilled. At the same time around 84,400 young people had not found a training place, up 19.9 per cent and the highest since 2010, with the share of unsuccessful applicants at 15.1 per cent, the highest since the end of the 2009 financial crisis.
- What it supports
- That the world's most admired apprenticeship system is failing to match young people to places in both directions at once: record unplaced applicants alongside tens of thousands of empty places. The bridge is not merely narrowing, it has stopped connecting.
- What it does not support
- That AI caused it. BIBB attributes the tension to matching problems, regional and occupational mismatch and economic conditions, and makes no AI claim.
Argued perspective#
Commission d acces a l information du Quebec (2025). L IA au travail: pour un meilleur encadrement
Memoire presented to the ministere du Travail, dated 27 January 2025, published 21 February 2025
- Method
- Submission by the provincial access and privacy regulator to a government consultation on the digital transformation of workplaces. A reasoned position, not a study.
- Finding
- On meaningful human intervention the Commission states, at page 4: 'lorsqu un humain enterine une decision proposee par un systeme d IA sans etudier l ensemble de l analyse, il existe un risque qu il demontre un biais d automatisation en faisant exagerement confiance a ce systeme'. It then lists four limits of sections 12.1 and 65.2: no duty to disclose at collection, no application to decisions that are not fully automated, no criterion for what counts as a decision, and the burden on the individual to ask. Recommendation 6 proposes prohibiting fully automated decisions with significant effects on employees.
- What it supports
- That a North American regulator has stated in writing that the human who ratifies without examining the whole analysis is where the safeguard fails, and has named automation bias as the mechanism. It is a regulator arguing against the sufficiency of its own jurisdiction s provision.
- What it does not support
- Anything measured. It is a submission, it contains no data on how often ratification without examination occurs, and its recommendations had not been enacted at the time of reading.
Institutional survey#
Eurostat (2025). Artificial intelligence use by enterprises (isoc_eb_ai), 2025 reference year
Eurostat news release, 11 December 2025, and statistical report KS-01-26-009-EN-N. Fieldwork Q1 2025
- Method
- Harmonised survey of enterprises with ten or more persons employed across NACE C to J, L to N and 95.1, excluding the financial sector, agriculture and the public sector. Approximately 157,000 enterprises surveyed from a population of about 1.53 million. An enterprise counts as a user if it used at least one of eight named AI technologies.
- Finding
- EU-27 average 19.95 per cent, up 6.47 points on 2024. Denmark first at 42.0 per cent, Finland second at 37.8, Sweden 35.0, Netherlands 33.2 (break in series), Spain 20.3, Portugal 11.5, Romania lowest at 5.2. Norway, reporting as a non-EU EEA state, 28.9 per cent. On individuals, a companion series puts Denmark highest in the EU at 48.4 per cent and Norway highest in Europe at 56.3.
- What it supports
- A comparable cross-national baseline for enterprise AI adoption, collected to a documented standard, which allows countries to be ranked against each other rather than against vendor surveys.
- What it does not support
- Depth of use. An enterprise counts if it used one of eight technologies once, so the measure says nothing about how many workers use AI, how often, or for what. It excludes the financial and public sectors and firms under ten employees. National statistics offices publish figures on different populations, so a national number and a Eurostat number for the same country are frequently not comparable.
Institutional survey#
General Authority for Statistics (GASTAT), Saudi Arabia (2025). Establishments ICT Access and Usage Statistics 2025
GASTAT, 2025
- Method
- National statistical survey of establishments, methodology stated as aligned with UNCTAD international standards. Enterprise-side only.
- Finding
- 33.1 per cent of establishments use artificial intelligence technologies, a growth of 20.0 per cent against 2024. By sector: information and communication 61.1 per cent, financial and insurance 52.9 per cent, education 51.0 per cent, manufacturing 30.7 per cent, wholesale and retail 30.2 per cent. The companion household and individuals survey for the same year contains no AI indicator at all.
- What it supports
- That one Gulf state measures enterprise AI adoption with a published, standards-aligned method. It is the only official national AI adoption statistic found across Saudi Arabia, the UAE and Qatar.
- What it does not support
- Anything about citizens, or about employment. Saudi Arabia measures enterprise adoption but not individual use, and publishes no AI employment series. Adoption is also self-reported use of a technology category, not a measure of capability or of value obtained.
Institutional survey#
Japan Institute for Labour Policy and Training (JILPT) (2025). Survey on the impact of workplace AI adoption on working styles (Research Series No. 256)
JILPT, published in Japanese, designed with the OECD
- Method
- 22,000 employees, stratified on 2020 Census occupation, employment type, sex and age. Fieldwork May to June 2024.
- Finding
- Only 12.9 percent report any firm AI use and 8.4 percent use it themselves. Among users, reports of improved job quality and wellbeing outweighed reports of decline, and the gain was markedly larger where the employer had consulted staff and funded training.
- What it supports
- That the effect of AI on how work feels is conditional on how the employer introduced it, not determined by the technology. Direct empirical support for the design-rather-than-drift argument, from a 22,000-person sample.
- What it does not support
- Long-run capability effects, and Japanese adoption rates are far below US levels so the user group is early and unusual.
Institutional modelling#
Lu, Y. and Gui, L. (Bulletin of the Chinese Academy of Sciences) (2025). Analysis of the impact of artificial intelligence technology on employment and income in China
Bulletin of the Chinese Academy of Sciences, 40(4), 642-651, published in Chinese
- Method
- Policy synthesis of the Chinese empirical literature and official statistics, under National Social Science Fund major project 23ZDA100.
- Finding
- Between 2018 and 2023 the substitution effect outweighed complementarity: a one percent rise in industrial robots reduced firm labour demand by 0.18 percent. Roughly 200 million people, 27 percent of employment, are in flexible work with 37 percent social-insurance coverage.
- What it supports
- That in the largest manufacturing economy the measured balance so far has been substitution rather than augmentation, and the policy response centres on social security redesign rather than retraining.
- What it does not support
- Comparability with service-sector generative AI in high-income economies. This is largely industrial robotics.
Operator account#
Moquim, S. A., Vice President, Saudi Data and Artificial Intelligence Authority, interviewed by Saeed al-Abyad (2025). SDAIA: Saudi AI Platform Baseer Boosts Crowd, Security Control During Hajj
Asharq Al-Awsat, dateline Jeddah, 4 June 2025; read at source 4 September 2026
- Method
- On-the-record interview with the operating authority's vice president. Descriptive throughout, with no evaluation and no data.
- Finding
- SDAIA operates Baseer, built with the Ministry of Interior, using AI algorithms and computer vision on live feeds to detect crowd density and distribution within the Grand Mosque and to pinpoint overcrowded zones such as the Tawaf area moment by moment, stated as enabling authorities to act swiftly to prevent overcrowding or stampedes. Companion platforms Sawaher and Sawaher Qiyada analyse live security camera feeds. The Smart Makkah Operations Center coordinates them, and biometric systems run at 12 international airports across 8 countries under the Makkah Route initiative.
- What it supports
- That the same public authority which published Saudi Arabia's AI ethics principles, including the requirement that irreversible or life-and-death decisions trigger human oversight and final determination, also builds and operates the system to which that requirement applies.
- What it does not support
- That the oversight works, or that anyone has tested it. Nothing here measures how often a commander overrides the system, what happens when the prediction is wrong, or whether unaided crowd-reading skill is maintained. The speaker heads the authority whose systems he is describing.
Institutional survey#
National Centre for Vocational Education Research (2025). Apprentices and trainees 2025
NCVER, Australia, March and June quarter releases 2025
- Method
- Official Australian administrative counts of apprentice and trainee training contracts.
- Finding
- 320,830 in-training contracts at 31 March 2025, down 7.9 per cent year on year, with trade down 3.2 per cent and non-trade down 17.9 per cent. By the June quarter the fall was 11.3 per cent, with non-trade down 20.2 per cent. NCVER attributes the non-trade decline partly to the conclusion of the Boosting Apprenticeship Commencements subsidy.
- What it supports
- That entry-level training volume responds sharply and quickly to subsidy withdrawal, and that the response is concentrated in the non-trade occupations closest to office work.
- What it does not support
- An AI effect. NCVER names policy change, and the timing follows the 2022 subsidy withdrawal rather than any AI milestone.
Institutional modelling#
Azim Premji University (2026). State of Working India 2026: Youth in the Labour Market
Azim Premji University, Bengaluru
- Method
- Analysis of National Sample Survey employment data 1983 to 2011, all quarters of the Periodic Labour Force Survey 2017 to 2024, plus AISHE, NCVT-MIS and CMIE-CPHS.
- Finding
- Roughly five million graduates enter the Indian labour market each year against about 2.8 million finding work, with graduate unemployment near 40 percent for 15 to 25 year olds. The report explicitly declines to attribute this to AI.
- What it supports
- That in the world's most populous labour market the early-career crisis is a demand-side bottleneck that long predates AI. A necessary corrective to reading every graduate hiring problem as an AI story.
- What it does not support
- Anything about AI's effect in India, which it deliberately does not claim.
Compiled review#
Cedefop (2026). Call for abstracts: apprenticeships in the age of AI
Cedefop, European Centre for the Development of Vocational Training, 5 August 2026
- Method
- Framing statement for the 2027 joint Cedefop and OECD apprenticeship symposium. Position paper, not a study.
- Finding
- Cedefop states: "AI takes over baseline tasks which were in many cases performed by apprentices or apprenticeship graduates who get entry-level roles once their programmes are completed. As apprenticeships continue to expand into new fields and occupations, they may also be exposed to a decline in entry-level openings." It adds that the contraction appears concentrated in white-collar roles, so the effect is likely to be uneven and occupation-specific rather than uniform.
- What it supports
- That a European Union agency has now named the mechanism this research describes, in its own words, and applied it specifically to apprenticeships. It is the clearest institutional statement that the training route and the entry-level job are the same thing.
- What it does not support
- Anything measured. It is a call for papers, which means Cedefop is asking the question rather than answering it, and it says the effect is uneven.
Institutional survey#
Census and Statistics Department, Hong Kong SAR (2026). Report on the Survey on Information Technology Usage and Penetration in the Business Sector, 2025 Edition
Census and Statistics Department, Hong Kong, released 27 February 2026
- Method
- National business survey, fieldwork March to December 2025. Full report and all thirty tables read at source, together with three companion household and ICT publications.
- Finding
- Artificial intelligence appears nowhere in the report: not in the tables, not in the explanatory notes, not in the definitions. The survey's ICT categories are cloud computing at 98.1 per cent, QR codes at 37.6 per cent, RFID at 20.3 per cent, internet of things at 7.4 per cent and augmented or virtual reality at 1.5 per cent. Hong Kong's statistical office measures AR and VR adoption and does not ask about AI at all. The 2023 edition also had no AI category, and no plan to add one has been announced.
- What it supports
- That Hong Kong publishes no official statistic on AI adoption or AI-related employment, which means every circulating Hong Kong AI adoption figure comes from a non-government survey with a self-selected sample.
- What it does not support
- That AI adoption in Hong Kong is low. It establishes that it is unmeasured by the government, which is a different and in some ways more useful fact.
Institutional modelling#
INSEE, France (2026). Note de conjoncture: digital investment, artificial intelligence and youth employment
Institut national de la statistique et des etudes economiques, March 2026, published in French
- Method
- National accounts and quarterly employment files, with an error-correction model estimated 1990Q1 to 2019Q4.
- Finding
- French employment of 15 to 29 year olds, excluding apprentices, fell 7.4 percent year on year in IT services, 5.8 percent in publishing and 3.7 percent in management consulting in Q4 2025, against minus 0.7 percent across the market sector overall.
- What it supports
- A European national-statistics office finding the same entry-level pattern reported in the US, which makes the signal considerably harder to dismiss as an American artefact.
- What it does not support
- That AI caused it. INSEE explicitly cautions against attributing the fall to AI alone.
Institutional survey#
International Labour Organization (2026). Global Employment Trends for Youth 2026: Back to the future
ILO, 11 August 2026
- Method
- ILO global modelled estimates of youth employment, unemployment and NEET status.
- Finding
- Global youth unemployment 12.4 per cent in 2025, 67 million people aged 15 to 24. The NEET rate is 20 per cent, over 257 million. Youth unemployment rose in 8 of 11 subregions between 2023 and 2025, with Northern America rising from 8.3 to 9.8 per cent. The ILO estimates 6.1 per cent of jobs held by workers aged 15 to 29 are in occupations highly exposed to AI-related change.
- What it supports
- That youth labour market conditions deteriorated across most of the world between 2023 and 2025, which is the context any national apprenticeship policy is operating in.
- What it does not support
- That AI caused it. The 6.1 per cent exposure figure is an occupational overlap measure, not a measured displacement.
Institutional modelling#
Jung, J. and Katz, R. (CEPAL / ECLAC) (2026). Impacto economico de la inteligencia artificial en America Latina (The economic impact of artificial intelligence in Latin America)
United Nations Economic Commission for Latin America and the Caribbean, published in Spanish
- Method
- Theoretical and econometric modelling of AI's macroeconomic effect through skilled-labour productivity across the region.
- Finding
- The gains run through skilled labour, and the binding constraint across Latin America is human-capital formation and low investment. The regional risk is UNDER-adoption rather than displacement.
- What it supports
- That the framing dominant in rich economies, where the worry is AI doing too much, inverts in middle-income economies, where the worry is that it will not arrive at all.
- What it does not support
- Firm-level outcomes; it is a macro model.
Institutional survey#
Manpower Research and Statistics Department, Ministry of Manpower, Singapore (2026). Labour Market Report, First Quarter 2026
Ministry of Manpower, Singapore, released 15 June 2026
- Method
- National firm survey by the labour ministry's statistics department. Note the companion press release omits the AI statistic entirely; it appears only in the full report.
- Finding
- 28.5 per cent of firms adopted AI in 2026, highest in information and communications at 74.1 per cent, professional services at 57.5 per cent and financial and insurance services at 56.4 per cent. Only 6.2 per cent reported AI-related reductions in headcount or hiring, against 18.9 per cent reporting redesign of job functions. The ministry's own reading: "AI is currently having a greater impact on job redesign and work processes than on broad-based job displacement."
- What it supports
- That a government labour ministry measuring this finds job redesign running roughly three times ahead of headcount reduction. It is a useful corrective to displacement forecasting.
- What it does not support
- What happens to capability. Redesign is not neutral: the question this research asks is which tasks the redesign removes, and a firm survey of headcount cannot answer it.
Compiled review#
Ministry of Communications and Information Technology, Qatar (2026). Artificial Intelligence in Qatar: Principles and Guidelines for Ethical Development and Deployment
MCIT Qatar, undated; read at source 28 August 2026
- Method
- National AI ethics guidance. Read in full at source, but with a provenance defect worth recording: the document carries no publication date, no version number and no reference number, and is not retrievable from the ministry's own website. It states that it "is legally non-binding, and adherence to it is voluntary".
- Finding
- Principle 8, assign ultimate accountability to humans, states that "AI systems should not be able to autonomously make decisions of significant consequence" and should "provide users with the ability to appeal or override decisions that have a substantial impact on individuals or society". The phrase human in the loop appears nowhere in the document, and Principle 7, develop a human-centered approach, concerns cultural values, feedback, diverse teams and accessibility rather than oversight.
- What it supports
- That Qatar requires humans to retain control of consequential decisions, in guidance.
- What it does not support
- Any competence duty. Qatar imposes no obligation that the humans exercising control be trained, assessed or kept current, and the document contains no reference to deskilling, over-reliance or automation bias. Widely circulated dates of May 2024 or 2025 for this document are unsourced: it carries no date at all.
Operator account#
Ministry of Interior, Kingdom of Saudi Arabia (2026). Smart Predictive Technologies Enhance Pilgrim Safety and Crowd Management
Saudi Press Agency, Makkah, 30 May 2026, 1447 AH Hajj season; read at source 4 September 2026
- Method
- The state news agency reporting the Ministry of Interior's own account of its Hajj operation. No methodology, no figures, no independent evaluation.
- Finding
- The ministry states it implemented predictive analytics to anticipate and mitigate congestion and hazardous conditions before they occurred, using an intelligent framework powered by AI and data analytics to accelerate strategic decision-making and, in its own words, to support field commanders. Oversight is described as running through a digital framework of performance indicators under the supervision of specialised personnel.
- What it supports
- That a state operating one of the largest recurring crowd operations in the world describes AI-driven prediction as an input to command decisions, and says so in the language of supporting rather than replacing the commander.
- What it does not support
- Anything measured. There is no figure for accuracy, no override rate, no comparison against unaided command judgement, and no independent evaluation of whether the oversight functions. It is a state news agency reporting a ministry on its own performance.
Institutional survey#
Pew Research Center (2026). Globally, more people expect AI to cause job loss than growth
Pew Research Center, 17 September 2026. Read at source 18 September 2026
- Method
- Nationally representative surveys of 42,151 adults in 37 countries, 18 high-income and 18 middle-income by World Bank classification plus the United States, fielded between 8 February and 13 May 2026, with US data drawn from two separate surveys. Probability-based samples, face to face or by telephone or online according to country.
- Finding
- In 34 of the 37 countries, majorities expect AI to lead to fewer jobs rather than more. In high-income countries a median of 55 per cent expect fewer jobs within 20 years, against 36 per cent in middle-income countries, where uncertainty is higher (34 per cent unsure against 22 per cent). Around seven in ten expect job losses in the United States, Australia and South Korea; US concern rose seven points from 2024 and among US adults aged 18 to 34 rose from 40 to 55 per cent. A median of 37 per cent are more concerned than excited about AI and 41 per cent equally both. People more often expect AI to widen than narrow the gap between rich and poor. In twelve middle-income countries surveyed on the point, a median of 43 per cent trusted China to regulate AI against 35 per cent for the United States and 34 per cent for the European Union.
- What it supports
- That the expectation of AI-driven job loss is now the majority view in most of the countries surveyed and strongest in the richest, and that the expectation is rising among the young in the United States.
- What it does not support
- What AI has done to employment: it measures expectation, not outcome, and the two must not be confused. The UK figures are not broken out in the headline report. Question wording differs by country and year, and the trust-in-regulators finding covers twelve middle-income countries only.
Institutional modelling#
Rodriguez-Fernandez, M. (Funcas) (2026). Inteligencia artificial y mercado de trabajo en Espana (Artificial intelligence and the labour market in Spain)
Funcas working paper, published in Spanish
- Method
- The Felten AI occupational exposure index remapped to Spanish occupational classifications, combined with the Q4 2025 Labour Force Survey.
- Finding
- Spain shows medium-high exposure at 27.4 percent but low automation risk at 5.9 percent, against an OECD average near 12 percent, because of its interpersonal and physical occupational mix.
- What it supports
- That national occupational structure, not technology, determines exposure. An economy weighted towards interpersonal and physical work is structurally less automatable.
- What it does not support
- Outcomes. Exposure indices remain estimates of what could be affected.
Where this evidence is used
The research that rests most on this section:
- AI and work in the Gulf
- Will AI replace my job?
- AI workforce strategy
- Do apprenticeships still work?
- Will AI replace entry-level jobs?
- AI and work, country by country
- Who supervises work they cannot do themselves?
- What is meaningful human oversight?
The other sections
- Judgement, thinking and offloading 79
- Learning, practice and expertise 92
- Human and AI collaboration 44
- Empathy, creativity and what stays human 36
- Work, jobs and the labour market 94
- The seven human capabilities 38
- The institutional record 180
- Professions and sectors 25
- Frontline, clinical and physical work 24
The whole base is also machine-readable at evidence.json under CC BY 4.0.
Reference · SS-2026-447 · How I use AI here
Cite this page
Hirji, R. (2026). The international picture. The SuperSkills evidence base, SS-2026-447. https://thesuperskills.com/research/evidence/international-evidence. Last reviewed 3 November 2026.
An evidence review by Rahim Hirji, not peer-reviewed research. For a material claim, cite the underlying study as well; every study here carries its own permanent link.
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