Classify the task by error cost
An error in reformatting a table is usually easier to detect and less consequential than an error in deciding whether a study supports a causal conclusion. The more a task affects the scientific claim, the more direct oversight it deserves.
Classify the task by verifiability
Some outputs can be checked mechanically: code syntax, duplicate references, or whether a DOI resolves. Other outputs require domain knowledge: whether a measurement is valid, whether confounding is plausible, whether an analysis matches the design, or whether a literature gap is real.
Low verifiability is a reason for more human involvement.
Keep primary evidence in the loop
A workflow becomes fragile when summaries of summaries replace source material. For important claims, the chain should still reach the paper, dataset, protocol, policy, or other primary record that supports the conclusion.
Document decisions that affect the analysis
Research depends on being able to explain how data were selected, transformed, analyzed, and interpreted. If an automated tool influences those decisions, researchers need enough documentation to reproduce and defend the process under the rules that govern the project.
Use institutional and publication policies as constraints
The technically possible workflow is not automatically the permitted one. Brown’s current AI-policy work, APA guidance, and journal policies illustrate that expectations vary by context. A researcher should identify those constraints before the workflow becomes dependent on a tool.
Keep the decision points human and auditable
Research contains moments where a choice changes the meaning of the result: defining the population, selecting outcomes, deciding what counts as evidence, handling missing data, choosing an analytic model, interpreting a surprising result, or deciding whether a conclusion is warranted. A tool can assist with mechanics around those decisions, but the reasoning should remain traceable to the researcher.
One practical safeguard is to separate generation from adjudication. A tool might propose search terms, coding categories, or possible explanations. The researcher then evaluates those suggestions against the literature, data, study design, and governing rules. This avoids turning a plausible suggestion into an unexamined premise.
Preserve a record of consequential choices
For important analytic or interpretive decisions, document what was decided, why, what alternatives were considered, and which source or result supports the choice. That record is useful even when no AI tool is involved. When automation is involved, it also prevents the reasoning chain from disappearing inside an ephemeral conversation.
