What is it about?
Artificial intelligence is rapidly transforming economic research by enabling analysis of massive datasets and revealing patterns that traditional methods might miss. AI tools can process satellite images to estimate economic activity, analyze text for market insights, and generate predictions faster than conventional approaches. Large language models can review literature and suggest hypotheses in minutes rather than weeks. However, AI creates significant challenges. These algorithms often work as "black boxes," making results difficult to verify and potentially reinforcing historical biases in areas like lending or hiring. The technology's benefits are distributed unevenly—some workers gain while others lose jobs to automation. Knowledge and computing power concentrate in wealthy institutions, potentially widening inequalities. Successfully integrating AI into economics requires methodological rigor, transparent and explainable models, enhanced researcher training, and policies addressing discrimination and inequality. AI is neither a miracle cure nor a threat, but a powerful tool requiring careful, ethical application to serve the broader public good.
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Why is it important?
This paper provides a comprehensive framework for understanding AI's transformation of economics at a critical moment when these technologies are rapidly entering mainstream research. Unlike purely technical treatments, it bridges three essential dimensions—methodology, ethics, and policy—showing how they interconnect. The work is particularly timely as economists worldwide grapple with integrating tools like ChatGPT and machine learning into their research while maintaining scientific rigor. By offering practical guidance on when and how to use AI responsibly, alongside clear warnings about pitfalls like algorithmic bias and concentration of power, this paper serves as an essential roadmap for researchers, policymakers, and institutions navigating this technological revolution.
Perspectives
As someone who has witnessed multiple methodological waves in economics, this moment feels particularly transformative. What excites me is AI's democratizing potential—enabling researchers in smaller institutions to access capabilities once limited to elite centers. Yet I worry these tools could instead deepen research inequalities without deliberate intervention. My experience bridging traditional econometrics and data science convinces me that success requires intellectual discipline regardless of tools. Economics' strength has always been asking the right causal questions and understanding institutions. We must ensure the next generation masters both computational skills and economic reasoning—technical proficiency without theoretical grounding produces sophisticated nonsense, while theory without modern tools increasingly limits our real-world impact.
Professor Imre Fertő
Eotvos Lorand Tudomanyegyetem
Read the Original
This page is a summary of: Mesterséges intelligencia a közgazdasági kutatásban, Külgazdaság, January 2025, Kopint Konjuktura Kutatasi Alapitvany,
DOI: 10.47630/kulg.2025.69.5-6.63.
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