The Tool Desk
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1. Define the claim, intended use, and scope
Before assessing methods, write down the paper’s main claim in a sentence. Identify the AI system’s intended use, the people or decisions it may affect, and the population or data distribution to which the authors say the result applies. Check whether the paper explains why machine learning is appropriate for the problem.
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A result on one dataset or benchmark does not, by itself, establish that the system will work for a different population, setting, or task. Keep the paper’s evidence and its broader implications separate: note what was actually studied and what the authors infer beyond it. The REFORMS checklist and guidance offer review questions on study goals, target populations or distributions, and the motivation for using machine learning.
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2. Trace the data from source to test set
Data choices shape what a model can learn and what its evaluation can show. Follow the data through collection, selection, preprocessing, and use in training and testing. Record any access limits and ask whether exclusions, missing values, or poor-quality inputs could change who or what the results represent.
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- Identity and provenance: Can you identify the datasets, their sources, and any links or DOIs the paper provides?
- Selection: Are collection criteria, inclusion and exclusion rules, and the target population described?
- Handling: Does the paper explain preprocessing, missing or low-quality inputs, and how training, validation, and test data were separated?
- Access: Can an independent researcher obtain the data? If not, does the paper explain the restriction and provide other ways to check the analysis?
NIST’s AI RMF Measure guidance emphasizes documentation of training and evaluation data, along with the tools and procedures used to measure performance. A data link is useful, but it does not tell you whether the data are complete, suitable, or representative of the stated scope.
3. Check whether the evaluation supports the claim
Identify the model, relevant implementation and training details, evaluation procedure, test sets, metrics, and tools. Then assess whether the metric matches the task and whether the test data reflect the conditions in the claim. Check that comparisons and baselines are appropriate rather than convenient.
- Are errors and limitations reported, not just aggregate performance?
- Does the paper examine uncertainty and relevant subgroups or data segments?
- Are the evaluation data independent of training and model selection in a way that supports the reported conclusion?
- Do the reported results justify the stated level of generalization?
NIST recommends reporting performance and errors across demographic groups and other segments relevant to deployment. REFORMS likewise prompts reviewers to consider evaluation, generalizability, and limitations. A strong overall score cannot substitute for evidence about the cases and groups that matter to the intended use.
4. Judge safety in the setting where the system may be used
Safety is not a property established by a benchmark score alone. Start with the system’s intended use and exposure: who could be affected, what decision or action follows an output, and what could happen if the output is wrong, unavailable, manipulated, or used outside the tested setting?
Look for hazards, failure cases, performance errors, and mitigations or human oversight that the study actually evaluated. Distinguish proposed safeguards from safeguards tested in the reported work. NIST’s AI RMF Manage guidance recommends prioritizing physical safety, legal liability, regulatory compliance, and negative impacts on people, groups, or society. Attribute any safety conclusion only to the populations, settings, hazards, and outcomes covered by evidence.
5. Decide whether the result is reproducible
Computational reproducibility asks whether an independent researcher can obtain the same reported results using the information and artifacts supplied with the work. The REFORMS authors define it as “the ability of an independent researcher to get the same results as reported in a paper or manuscript” in their 2024 Science Advances article.
Check whether the paper provides enough detail to identify and use the data, code, software and computing environment, and procedural steps needed to regenerate the claimed results. Distinguish a link from a complete, runnable artifact—and both from a result that has actually been reproduced. The paper and its checklist cannot establish that code is correct.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11When data or other materials cannot be released, consider the reason and scope of the restriction and what substitute verification is available. A reproduction script, detailed procedures, or materials sufficient to verify other parts of the analysis may still help. Restricted access alone does not prove a study is irreproducible; adequacy depends on the field and study design. NIST’s Research Data Framework recognizes that reproducibility expectations vary across disciplines. Its AI RMF Measure guidance notes: “Documentation enables repeatability and consistency, and can enhance AI risk management decisions.”
6. Choose a checklist that fits the paper
Machine-learning-based science: REFORMS
REFORMS is a reporting checklist for machine-learning-based science, developed by consensus among 19 researchers in 2024, representing computer science, data science, mathematics, social sciences, and biomedical sciences. Its 32 questions are paired with guidance. Use the applicable questions to structure your review; completion does not prove that the methods are sound or the claim is true.
AI interventions in randomized clinical trials: CONSORT-AI
For a randomized clinical trial involving an AI intervention, use CONSORT-AI alongside applicable CONSORT guidance. The 2020 extension recommends 14 additional checklist items. They address intended use; how input data are acquired and selected; handling poor-quality or unavailable inputs; human–AI interaction; system outputs and downstream decisions; and analysis of performance errors. It is a clinical-trial reporting extension, not a universal checklist for every AI paper.
Risk and measurement: NIST AI RMF
NIST’s AI Risk Management Framework provides a risk-and-evaluation lens for examining the model, data, training and evaluation details, metrics, error and performance across relevant groups, and ethical considerations. Use it to ask what risks arise in the proposed context and whether documentation supports informed assessment. It complements study-specific reporting guidance rather than replacing it.
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7. Check disclosure when AI helped conduct the research
If AI tools contributed to the analysis or reporting, look for identification of the model, an explanation of prompting, and a description of how outputs were validated. Those details let readers assess how the tool entered the research workflow and what checks were applied.
Compare papers on the same evidence
When reviewing multiple papers, use the same axes rather than comparing headline scores alone:
- Claim, intended use, and target population or data distribution.
- Data provenance, quality, selection, and accessibility.
- Evaluation design, metrics, baselines, errors, and subgroup evidence.
- People affected, hazards, downstream decisions, and tested safeguards.
- Completeness and verifiability of the materials needed to reproduce results.
- For clinical trials, input eligibility and quality handling, human–AI interaction, and the role of outputs in care.
These are review prompts, not a universal pass/fail score. Checklist completion, open artifacts, and clear reporting are useful evidence about a paper’s transparency; none alone establishes truth, safety, or successful reproduction.
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