--- title: "AI-induced labour market shocks and inclusive labour market resilience: an LLM-assisted rapid causal evidence synthesis using directed acyclic graphs (ESC-DAGs) · AI Fault Line" url: https://aifaultline.com/research/p/doi-10-17605-osf-io-gs8c5/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # AI-induced labour market shocks and inclusive labour market resilience: an LLM-assisted rapid causal evidence synthesis using directed acyclic graphs (ESC-DAGs) М. В. Козлова, Ye. V. Varavin · Open Science Framework · 11 Oct 2026 [Read original source](https://doi.org/10.17605/osf.io/gs8c5) Open access Where this sits in the debate - [Will AI destroy more jobs than it creates?](https://aifaultline.com/debate/jobs/) - [Will AI widen inequality?](https://aifaultline.com/debate/inequality/) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## Key takeaway [AI-written](https://aifaultline.com/about/) A protocol for a rapid systematic review of causal evidence on how robots, software automation, machine learning and generative AI affect employment, hours, hiring and wages, and how those effects are distributed by age, skill, gender and region. Studies will be graded by identification strength, with LLM-assisted screening under a pre-set validation rule. ## Abstract Protocol of a rapid systematic review that synthesises causal evidence on how AI-related technology shocks (industrial robots, software automation, machine learning and generative AI) affect inclusive labour market resilience: resistance, recovery, re-orientation and renewal of employment, hours, hiring, wages and reallocation, and the distribution of these effects across age, skill, gender and region. Studies are classified by identification tier and synthesised with evidence synthesis for constructing directed acyclic graphs (ESC-DAG) against a prior DAG. Screening and extraction are LLM-assisted (Claude, Anthropic) under a pre-specified validation rule, following the Cochrane rapid review guidance and the 2025 position statement on AI use in evidence synthesis. ## About this publication **Academic journal.** Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth. Published 2026 (recently) Citations 0 (OpenAlex) 0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison. [Chart: published 2026] ## More on this topic Related by theme, not necessarily by method or conclusion. - [The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers](https://aifaultline.com/research/p/doi-10-2139-ssrn-4945566/) SSRN · 2024 - [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://aifaultline.com/research/p/doi-10-48550-arxiv-2303-10130/) arXiv · 2023 - [Experimental evidence on the productivity effects of generative artificial intelligence](https://aifaultline.com/research/p/doi-10-1126-science-adh2586/) Science · 2023 - [The simple macroeconomics of AI](https://aifaultline.com/research/p/doi-10-1093-epolic-eiae042/) Economic Policy · 2024 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).