AI research ethics checklist
Using AI in research — to generate data, assist analysis, draft text, or simulate participants — raises ethics questions that traditional protocols did not anticipate. Plausible-but-fabricated data, undisclosed AI authorship, participant data leaking into third-party tools, and irreproducible AI-influenced results all threaten research integrity. This checklist walks you through an IRB-style self-assessment tailored to your research type, how you are using AI, and whether human participants are involved, so you catch the issues before submission or publication.
How it works
You describe the project: the research type (empirical, qualitative, computational, or literature-based), how AI is used (data generation, analysis assistance, writing support, or participant simulation), and whether human participants are involved. The tool filters a master list of ethics items into the relevant categories — informed consent and participant data, research integrity and fabrication risk, attribution and transparency, and reproducibility — flags the critical ones, and tracks your completion. You can export the finished checklist into your IRB application, methods section, or supervisor review.
Tips and notes
- Separate AI-generated from collected data. Label it, store it apart, and never let synthetic content masquerade as evidence.
- Disclose specifically. Name the tool, its version, and the exact role it played — not a vague “AI was used” line.
- Mind participant consent. Sending participant data to a third-party model can exceed the original consent and retention assumptions.
- Preserve reproducibility. Record prompts, model versions, and parameters; AI outputs drift, so an undocumented method is not reproducible.