Digital Tools, Historical Rules: Navigating AI in Economic History

Christoffer Friedl () and Erik Lakomaa ()
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Christoffer Friedl: Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden
Erik Lakomaa: Institute for Economic and Business History Research, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden

No 2026:1, SSE Working Paper Series in Economic History from Stockholm School of Economics

Abstract: Recent advances in generative AI promise to transform economic history by unlocking textual archives, quantifying complex historical concepts, and expanding the empirical toolkit. Yet these opportunities raise fundamental methodological questions. This paper evaluates large language models (LLMs) as tools for digital economic history, comparing their algorithmic foundations with core principles of historical scholarship. We identify three key areas of tension: compromised source criticism due to unclear data provenance, opacity in model training that hinders scholarly review, and probabilistic output that challenges reproducibility. We also diagnose two specific AI-related problems: the “secondary source paradigm,” in which LLMs rely on representations of texts rather than primary sources, and “AI intrusive thoughts,” or the inability of LLMs to respect historical temporality. While these limitations mean AI cannot replace human historical inquiry, we demonstrate practical ways in which it can augment it, such as contextual corpus searches, assistance with digitization workflows, and feedback on methodological framing. Drawing from applications in financial history and correspondence analysis, we propose five guidelines for the responsible and sustainable integration of AI tools into economic history research. The paper is especially relevant to researchers working in developing-country contexts, where archival gaps are common but methodological rigor remains essential. Our contribution bridges technical and historiographical perspectives to support responsible AI adoption in the field.

Keywords: Artificial Intelligence; Economic History; Digital Methods; Research Ethics; Historical Methodology (search for similar items in EconPapers)
JEL-codes: A00 B40 N01 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2025-08-02
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:haechi:2026_001

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