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The Utility of AI Tools in Auditing Adherence to Pre-Analysis Plans

Author

Listed:
  • Jeffrey Clemens
  • Anwita Mahajan

Abstract

Pre-analysis plans (PAPs) can improve research reproducibility by reducing researchers’ degrees of freedom, but their value depends on adherence. We argue that large language models (LLMs) can provide an efficient, scalable, and systematic way for authors and reviewers to assess adherence to PAPs. In an application to our own research, an LLM systematically identifies precommitted design choices, evaluates deviations, and diagnoses gaps in pre-specification, substantially reducing the human labor required for these tasks. However, variability in audit output across LLMs underscores the continued importance of human judgment. We discuss implications for best practices in AI-assisted PAP auditing.

Suggested Citation

  • Jeffrey Clemens & Anwita Mahajan, 2026. "The Utility of AI Tools in Auditing Adherence to Pre-Analysis Plans," NBER Working Papers 35719, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35719
    Note: EH LS PE
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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