Concern 734 · Working draft · editorial review in progress
Reproducibility crisis extension
AI-related reproducibility risk arises when important uses of a model are not documented well enough for others to understand or repeat a research process.
Understanding the concern
A researcher may use AI to label data, generate code or make analytical choices. If the model version, instructions, settings and checks are missing, another team may obtain different results without knowing why. An online service may also change after the study, making the original process difficult to recreate.
Reporting guidance asks authors to explain relevant AI use and remain accountable for the work. This is like writing a recipe that names an ingredient but not its amount or preparation. Disclosure alone is not enough: researchers may need to preserve code, outputs and validation records within privacy and licensing limits. The goal is a traceable method, not simply an 'AI used' label.
An example
A paper's data labels were generated by a changing online model, but the authors did not record the version or instructions.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
JAMA's guidance supports transparent methods. It does not guarantee exact reproducibility or override privacy and data-access restrictions.
JAMA 2024 · Editorial guidance
Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance ↗ (opens in a new tab)Transparent reporting and accountability for AI methods and writing. Not proof that disclosure alone makes research reproducible.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What records would another team need to understand and check the AI-dependent steps in this study?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Learning together
Learning together means treating experience as something a group can examine and improve. It values honest feedback over appearing successful.
Agree on a question, try something manageable, and compare what different people observed. Share failures as well as gains. Decide together what the evidence supports and what remains uncertain.
Ask together: What have we learned together that should guide the next step?
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
Search for more writing
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