arXiv AI
Jul 22
Global Automation Atlas
arXiv:2605. 17086v2 Announce Type: replace-cross Abstract: Automation can displace or complement labour, but this need not be constant across economies.
Measuring Digital Labour Market Transitions with a Digital Semantic Score: An AI-Based Methodology Applied to the Dutch Labour Market
The paper presents an AI-based methodology that uses embedding-based similarity search and large language model classification to map Dutch job profiles to harmonised ESCO occupations. It introduces a Digital Semantic Score, which quantifies the association of job titles and skills with digital concepts relative to a non-digital reference, moving beyond keyword-based approaches. The study finds that digitalisation is unevenly distributed across occupations, with managerial, professional, and ICT roles showing the strongest digital language, while career-transition and skill analyses reveal pathway-dependent movement toward digital work and a multidimensional nature of digital capability.
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arXiv AI
Jul 29
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
arXiv:2603. 14558v3 Announce Type: replace Abstract: Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes.
arXiv AI
Jun 9
Complement or substitute? How AI increases the demand for human skills
arXiv:2412. 19754v4 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) is transforming the nature of work, yet there is limited empirical evidence on how it affects demand for human skills.
arXiv AI
Sep 10
Who Delegates to AI? Evidence from Agent Configurations in Github
The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
arXiv AI
Sep 16
AI-Driven Feedback Systems, Digital Labour, and Silent Quitting: Transforming African Workplaces
arXiv:2609.16192v1 Announce Type: cross Abstract: The current trend of digitalisation has revolutionised the organisation of work and the way it is measured and performed across the globe, with AI be...
arXiv AI
Jun 29
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
arXiv:2308. 05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics.
arXiv AI
Aug 13
A Conceptual Framework for Enhancing Workforce Readiness for Smart Manufacturing in the AI Era
arXiv:2608. 11540v1 Announce Type: cross Abstract: The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditional engineering and technology education.
arXiv AI
Jul 15
Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring
arXiv:2607. 12588v1 Announce Type: new Abstract: According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act.
arXiv AI
Jun 2
An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification
arXiv:2606. 01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness.
arXiv AI
3d ago
A Reusable Semantic Web Framework for Evidence-Grounded Fundamental Rights Impact Assessments under the EU AI Act
The paper introduces a reusable Semantic Web framework that aggregates fragmented evidence needed for Fundamental Rights Impact Assessments under the EU AI Act, focusing on high‑risk public sector categories such as employment and worker management and access to essential public services. A curated 150‑record corpus is annotated across four axes and serialized into a SPARQL‑queryable knowledge graph of 1,351 RDF triples, enabling five demonstration scenarios that retrieve 103 records (68.7% coverage). Evaluation against a 69‑record gold standard shows that LLM‑assisted classification in the employment domain yields a low κ of 0.045, highlighting challenges in automated fairness‑related evidence retrieval, while all artefacts are released openly for regulators, authorities, and SMEs.
arXiv Computation and Language
Sep 14
ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge
arXiv:2609. 12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models.
OpenAI Blog
Jun 29
Mapping Europe’s AI Workforce Opportunity
A new OpenAI report maps how AI could reshape jobs across the EU, highlighting which occupations may face automation, growth, or workflow changes.