Review participant fraud and response quality in AI interviews
Use screening, AI quality signals, transcript review, and provider-specific recruitment controls to improve AI-moderated research data.
Low-quality and fraudulent responses are a risk in any online research, especially with paid panels. AI-moderated interviews help because the interviewer follows up: a participant who is making things up has to keep inventing details, and it shows. Versive adds automatic quality and fraud checks on every completed interview.
How Versive flags problem responses
Every completed interview gets an AI summary with:
- A quality rating, with the reason behind it
- A fraud flag when the responses look suspicious, with an explanation
Use these to decide which interviews to review first, then read the transcript before you make a call. The AI can misread a short but genuine answer.
Keep the wrong people out
- Screen first. Ask about recent behavior before the main questions, without revealing which answer qualifies. See How to write screener questions.
- Use panel targeting. When recruiting through Prolific or Respondent, narrow the audience with their filters and screeners before anyone reaches your study. See how to recruit research participants.
- Ask about specific events. "Tell me about the last time you changed your subscription" is much harder to fake than "What do you think about subscriptions?", and the AI interviewer will ask for details.
Tell fraud apart from other problems
Not every weak interview is fraud:
| What you see | Likely cause | What to do |
|---|---|---|
| Answers unrelated to the questions | Low effort or fraud | Read the full transcript and exclude if needed |
| No experience with the product category | Wrong audience | Tighten screening or targeting |
| Several people misread the same question | Unclear question | Reword it and pilot again |
| Garbled spoken answers | Audio or transcription problem | Listen to the recording |
A participant who keeps saying "I don't know" may simply never have used the feature you're asking about. That's worth knowing too.
Review the first responses before scaling
Before recruiting at full volume, read the first handful of completed interviews: the summary, the quality rating, any fraud flag, and the transcript. Catching a screening gap after ten interviews is much cheaper than finding it after two hundred.
Exclude consistently and report it
Apply the same exclusion criteria to everyone, and keep a note of which interviews you excluded and why. For paid panels, review submissions before approving payment. In the final report, say how many responses you collected, how many you included, and why. See Thematic analysis, automated.
Frequently asked questions
Does Versive detect fraudulent or low-quality responses?
Yes. Versive analyzes every completed interview, gives it a quality rating, and flags possible fraud with an explanation of why. Use the flags to decide which interviews to review first.
Is a fraud flag proof that a participant cheated?
No. A flag is an AI assessment, not proof. A short but honest answer, a confusing question, or poor audio can all look suspicious, so check the transcript before excluding anyone.
How do I keep low-quality participants out of a study?
Screen for relevant experience before the main questions, ask about specific recent events that are hard to fake, and review the first few responses before recruiting at full volume.
Full reference
Results & insights
Keep reading
AI-moderated research for agencies: clients, branding, and licensing
Plan agency research in Versive: separate client projects, customize studies, share deliverables, and evaluate licensing and white-label requirements.
Embed an interview inside your product
Add an in-product survey embed with the Versive SDK: modal or inline, zero-code attributes, and the domain security to set up first.
How many participants do you need for user research?
How many participants you need for usability testing and user research, by method: qualitative saturation, the 5-user rule, and quantitative sample sizing.
