Artificial Intelligence (AI) Usage Policy

Authors may use generative AI or AI-assisted technologies to support activities such as language improvement, literature organization, idea development, data exploration, coding support, visualization, or manuscript preparation. AI tools must not replace the authors' critical thinking, economic reasoning, scholarly judgment, econometric interpretation, policy analysis, or original contribution.

Authors are responsible for:

• verifying factual accuracy and checking references generated by AI;
• reviewing and substantially editing AI-assisted content;
• checking for bias, hallucination, fabricated citations, numerical errors, or misleading economic interpretations;
• protecting confidential, proprietary, copyrighted, personal, and unpublished data;
• independently verifying AI-generated calculations, econometric outputs, code, policy interpretations, and analytical conclusions; and
• ensuring that the final manuscript represents the authors' own scholarly work.

Meaningful use of generative AI in manuscript preparation must be disclosed in a separate AI Declaration. The declaration should identify the tool used, its purpose, and the extent of human review and oversight.

Basic spelling, grammar, or punctuation checks do not normally require disclosure. If AI forms part of the research method, econometric analysis, forecasting, economic modeling, policy simulation, text analysis, causal inference workflow, or data processing, its use must be described in sufficient detail in the Methods section.

AI tools, chatbots, language models, machine-learning systems, and other automated technologies must not be listed as authors or co-authors. Authorship requires human responsibility for the integrity of the work, approval of the final manuscript, accountability for its content, and the ability to respond to questions regarding the research.

Generative AI must not be used to create, manipulate, obscure, remove, or introduce features in charts, maps, tables, economic figures, data visualizations, or other empirical evidence in a way that misrepresents the underlying data.

AI-generated illustrations that are purely explanatory or conceptual must be clearly identified and must not be presented as original empirical evidence.

An exception may apply when AI-assisted visualization, classification, forecasting, mapping, or pattern recognition is part of the research design or methodology. In such cases, authors must describe the tool, model/version, procedure, and its role in generating or interpreting research data in the Methods section.

Submitted manuscripts are confidential documents. Reviewers must not upload manuscripts, manuscript excerpts, unpublished economic datasets, analytical code, model outputs, supporting files, or review reports into public generative AI systems.

Peer review is a human scholarly responsibility. AI tools must not be used to replace independent scientific assessment, evaluate analytical quality autonomously, determine economic relevance, or determine review recommendations. Reviewers remain personally responsible for the content and integrity of their reports.

Editors must not upload submitted manuscripts, confidential datasets, unpublished analyses, editorial correspondence, reviewer reports, or editorial decision letters into public generative AI systems.

AI must not replace human editorial judgment or be used to determine acceptance, revision, or rejection. Editors remain fully responsible for scientific evaluation, methodological assessment, communication, and final publication decisions.

Economic research may involve household data, firm-level records, banking information, tax records, administrative databases, transaction data, labor-market information, commercial datasets, policy documents, or other confidential and proprietary information. Such materials must not be entered into public AI systems where doing so would violate privacy, confidentiality, data-use agreements, licensing restrictions, or applicable law.

• Personally identifiable and confidential economic information must be protected.
• De-identification or anonymization must be applied where appropriate.
• Institutional, governmental, contractual, and licensing restrictions on data processing must be respected.
• Restricted datasets must not be uploaded to public AI platforms without appropriate authorization.
• AI-assisted analysis must preserve data provenance and research traceability.
• Restrictions affecting replication or data sharing should be stated clearly.

When artificial intelligence, machine learning, natural language processing, predictive analytics, automated forecasting, or algorithmic decision-support systems are central to the research, authors must provide sufficient methodological detail to allow scientific evaluation and reproducibility.

• Identify the model, algorithm, software, or platform used.
• Describe training, validation, testing, and external validation procedures where applicable.
• Explain dataset composition, unit of analysis, inclusion and exclusion criteria, and preprocessing procedures.
• Report relevant performance measures, uncertainty, error metrics, and model limitations.
• Describe steps taken to assess overfitting, data leakage, robustness, calibration, and generalizability.
• Explain how economic theory or established empirical evidence informed model interpretation where relevant.
• Provide relevant parameter settings, analytical code, computational environments, or reproducibility information where possible.
• Explain human oversight and how model outputs were economically interpreted.

AI systems used in economic research may reproduce or amplify biases present in historical datasets, administrative systems, financial records, labor-market data, policy environments, or sampling processes.

Authors should assess relevant bias and distributional effects where scientifically appropriate. Potential impacts across income groups, regions, genders, firms, industries, demographic populations, or other economically relevant groups should be discussed where AI-assisted findings could affect interpretation or policy recommendations.

AI-generated outputs must not be treated as substitutes for economic reasoning, econometric validation, institutional knowledge, policy expertise, or appropriate human judgment.

Authors must avoid overclaiming causal effects, predictive accuracy, policy impact, market applicability, or generalizability when such claims are not supported by appropriate evidence. Any policy, financial, development, labor-market, or economic recommendation derived from AI-assisted research remains subject to human interpretation and scholarly accountability.