03 · What You Need to Know
A Systematic Method for Investigating AI and Human Expert Errors
Begin by Converting the Disagreement Into a Testable Claim
Statements such as "AI says the analysis is wrong" or "The supervisor disagrees with the model" do not identify what needs verification.
A productive investigation begins by expressing the disagreement as a specific proposition.
For example, suppose AI recommends a mixed-effects model while a human expert recommends ordinary least squares regression. The actual disagreement might concern whether observations are independent, whether the outcome variable has a suitable distribution, or whether the study requires estimates of within-participant change.
Each possibility requires a different examination.
A useful formulation is: "The AI claims that observations from the same participant are statistically dependent and that the current analysis does not account for this dependence."
That statement identifies something that can be checked against the data structure and the analytical specification.
Without this step, researchers risk comparing two broad recommendations while overlooking the narrower factual issue that separates them.
Check Whether Both Advisers Are Working With the Same Information
Many apparent disagreements arise because AI and human experts have received different descriptions of the research.
An AI system may be given a short prompt stating that 200 observations were collected. The human expert may know that those observations came from 50 participants measured four times each.
If the AI assumes independent observations, its recommendation may be inappropriate. The problem is not necessarily a failure to understand statistical theory. It may be an incorrect assumption about the study.
The reverse can also occur. A human expert may be unaware that the dataset was updated, a variable was recoded, or the research question changed.
Before evaluating correctness, establish the information available to each adviser at the time of the recommendation.
Record relevant differences in the dataset, research objectives, measurement procedures, analytical assumptions, and institutional constraints.
This step can reveal that the disagreement resulted from incomplete communication rather than a substantive scientific error.
Distinguish Factual Errors From Methodological Disagreements
Not all research disagreements have the same evidentiary structure.
| Type of disagreement |
Illustrative claim |
How to investigate |
| Factual |
A publication reports a particular finding. |
Read the original publication and verify the reported result. |
| Computational |
A statistical coefficient was calculated incorrectly. |
Reproduce the calculation using the original data and verified code. |
| Methodological |
A proposed analysis violates a statistical assumption. |
Examine the study design, model assumptions, and relevant methodological literature. |
| Interpretive |
The findings establish a causal relationship. |
Evaluate the research design, identification assumptions, and alternative explanations. |
| Policy-related |
A particular research procedure does not require ethics review. |
Consult the applicable institutional or regulatory authority. |
| Strategic |
One defensible method is preferable to another. |
Compare research objectives, trade-offs, resources, and practical implications. |
A factual or computational error may be resolved through direct verification. A strategic disagreement may remain open even after all relevant facts have been established.
Researchers should therefore avoid describing every difference in professional judgment as a mistake.
Identify the Assumptions That Make Each Recommendation Valid
A recommendation can be correct under one set of conditions and inappropriate under another.
Consider a disagreement about whether a parametric statistical test should be used. One adviser may assume the data satisfy the relevant distributional and dependence requirements. The other may assume those requirements are violated.
The investigation should establish which assumptions actually apply to the study and whether any violations materially affect the proposed analysis.
Assumptions may concern sampling, measurement, missing data, model specification, independence, or the relationship between observed variables and theoretical constructs.
Some assumptions can be examined empirically. Others, particularly causal identification assumptions, may not be fully testable using the available data.
When assumptions cannot be conclusively verified, researchers should evaluate their plausibility, consider sensitivity analyses where appropriate, and acknowledge the remaining uncertainty.
This prevents a common error: treating a technically correct general statement as a universally applicable research recommendation.
Trace Every Consequential Claim to Its Original Evidence
Neither an AI-generated explanation nor a human expert's recollection should automatically substitute for an authoritative source.
If the disagreement concerns a published finding, retrieve the original article and examine its methods and results. If it concerns software behavior, consult official documentation and test the relevant function. If it concerns journal requirements, check the journal's current instructions.
AI-generated citations require particular scrutiny because language models can produce nonexistent references or misrepresent genuine publications.
The International Committee of Medical Journal Editors cautions that AI-generated scholarly material may be incorrect, incomplete, or biased despite sounding authoritative. Its recommendations place responsibility for the accuracy of AI-assisted submitted work on human authors. The ICMJE's guidance on AI use by authors is relevant here, although individual journal policies must also be checked.
Human experts may also misremember publication details or interpret findings outside their original context.
For either source, the important question is whether the original evidence supports the precise claim being made.
This is why researchers should request the evidence underlying both AI and human recommendations when a consequential disagreement arises.
Reproduce Calculations Instead of Comparing Explanations
When the disagreement concerns a numerical result, reproducibility can provide a particularly strong form of verification.
Suppose AI claims that a reported percentage is incorrect while the human expert believes the calculation is accurate. The appropriate response is to examine the numerator, denominator, exclusion rules, and formula used to produce the result.
The same principle applies to statistical code, data transformations, descriptive statistics, and model estimates.
A useful verification procedure includes inspecting the original inputs, identifying the expected calculation, executing it independently, and comparing the result with the disputed output.
For consequential analyses, an independent implementation may be preferable to simply rerunning the same script. Repeating an erroneous procedure can reproduce the error perfectly.
Independent verification might involve a separately written script, a validated software procedure, or manual calculation for a small subset of observations.
Agreement between implementations increases confidence when the implementations are sufficiently independent and their assumptions are appropriate. It does not automatically establish that the underlying research design is valid.
Determine Whether the AI or Human Expert Misinterpreted the Research Question
A recommendation can be technically sound but directed at the wrong objective.
Suppose a study asks how students describe their experiences of academic belonging. AI recommends sentiment analysis because the dataset contains textual responses. A qualitative expert recommends thematic analysis because the objective is to interpret patterns of meaning across participants' accounts.
The AI suggestion may be computationally feasible, but feasibility alone does not establish methodological suitability.
The disagreement should be examined in relation to the research question, epistemological commitments, analytical objectives, and type of inference sought.
In this example, the researcher should not declare that sentiment analysis is inherently wrong. Rather, it may answer a different question from the one the study was designed to investigate.
This distinction is particularly important in interdisciplinary research, where familiar terminology can conceal differences in methodological purpose.
Check for Errors in the AI Prompt and Supplied Context
Before concluding that an AI system made a reasoning error, inspect the information provided to it.
A prompt may contain an incorrect sample size, omit the study's unit of analysis, misstate the measurement scale, or fail to mention that observations are paired.
If the AI response follows from incorrect input, the resulting recommendation may be unreliable even if the model applied an otherwise valid principle.
Correcting the prompt and asking the model to reassess can help identify whether missing context contributed to the disagreement.
However, a revised AI answer is not independent confirmation. The corrected recommendation still requires verification against authoritative evidence.
Researchers should also avoid providing confidential participant information or restricted manuscripts merely to obtain a more detailed AI response. Data protection and institutional permissions remain applicable throughout the investigation.
Evaluate Whether the Human Expert Has Relevant Expertise
Human expertise is valuable, but its reliability depends partly on the match between the expert's competence and the question being asked.
A senior researcher may have extensive experience designing experiments while having limited experience with a specialized machine-learning technique. A computational scientist may understand model optimization while lacking expertise in qualitative research ethics.
Kahneman and Klein (2009) emphasize that reliable intuitive expertise depends on conditions that support learning from predictable regularities and meaningful feedback. Their analysis also cautions against equating subjective confidence with judgment accuracy.
These findings do not provide a universal test for identifying expert mistakes. They do, however, support examining whether an expert's experience is relevant to the disputed problem.
When the disagreement falls outside the human adviser's principal expertise, an additional specialist opinion may be particularly useful.
Recognize That Both Sources Can Be Wrong
Researchers sometimes approach a disagreement as though one recommendation must be correct simply because the alternatives conflict.
That assumption is unwarranted.
An AI system may recommend an inappropriate model because it misunderstands the data structure. A human expert may reject the model for an equally mistaken reason.
Independent examination might show that neither proposed method adequately addresses the research question.
For example, both advisers might overlook substantial missing-data problems, measurement error, or a mismatch between the study design and the intended causal interpretation.
Resolving the disagreement may therefore require developing a third approach rather than selecting one of the original recommendations.
The possibility that AI can identify an error overlooked by researchers should not be confused with an assumption that AI and human recommendations exhaust the available alternatives.
Use Sensitivity Analyses When the Disagreement Concerns Defensible Alternatives
Sometimes independent examination establishes that more than one method is scientifically defensible.
For example, two approaches to handling missing observations may rely on different assumptions. Neither can be declared universally superior without considering the missingness mechanism, available variables, research objectives, and implementation details.
Where appropriate, sensitivity analyses can show whether substantive conclusions depend on the disputed analytical choice.
If conclusions remain similar across reasonable specifications, the disagreement may have limited practical consequences. If conclusions change substantially, the choice requires closer examination and transparent reporting.
Sensitivity analysis is not a mechanism for selecting whichever result is more favorable. It should be guided by scientifically defensible alternatives and, where relevant, the prespecified analysis plan.
The results should be interpreted in relation to the assumptions each approach requires.
Separate the Error's Existence From Its Consequences
Finding a mistake does not automatically establish that the research conclusions are invalid.
A coding error might affect a descriptive table without changing the main analysis. Alternatively, a seemingly minor error in outcome classification could materially alter the study's conclusions.
After verifying an error, determine its scope and consequences.
Relevant questions include which outputs are affected, whether the error propagates through subsequent analyses, whether conclusions change, and whether participant protections or reporting obligations are implicated.
This assessment determines the appropriate corrective action.
A verified mistake may require a local code correction, reanalysis, revision of a manuscript, consultation with collaborators, or notification of a journal if the work has already been submitted or published.
The appropriate response depends on the stage of the research and the seriousness of the error.
Document How the Error Was Established
A defensible investigation should produce more than a conclusion that AI or a human expert was wrong.
Record the disputed claim, information available to each adviser, evidence examined, verification procedure, findings, and resulting research decision.
When AI materially contributed to identifying the problem, retain relevant interaction details as permitted by institutional policy and confidentiality requirements.
This documentation can support later methodological review and explain why a research decision changed after AI consultation.
It can also help prevent the same error from recurring in subsequent analyses.
Watch Out
Do not treat agreement from another AI system, a confident explanation, or a second unverified opinion as conclusive proof. For consequential research decisions, verification should rely on evidence or procedures sufficiently independent of the disputed recommendations.
