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
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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.
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Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
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- 2026 (recently)
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