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Tutorial

Using AI in Qualitative Research Data Processing, Ethically

Dr. Loreta TauginienėEnglishData analysis

TL;DR

1-min read

Qualitative researchers increasingly use AI to transcribe interviews and prepare data for analysis, but these shortcuts carry risks that are easy to miss. In this ethics-focused tutorial, Dr. Loreta Tauginienė, Associate Professor at Kaunas University of Technology, Editor-in-Chief of the Journal of Academic Ethics, COPE Council member and Advisory Board member of the French Office for Research Integrity, treats AI as one instrument among many rather than a replacement for the researcher's judgment. It suits qualitative and mixed-methods researchers, doctoral candidates preparing ethics applications and ethics committee members who handle sensitive interview or focus-group data.

Key takeaways

  • AI should be positioned as a supporting tool, with the qualitative researcher remaining responsible for interpretation and decisions.
  • Before sending interview audio to an AI transcription service, assess where the audio is stored, who can access it and whether participants consented to third-party processing.
  • Many ethics boards expect you to name the tool, explain its data handling and check transcripts manually, treating AI output as a draft to verify.
  • Automated anonymisation can miss indirect identifiers such as job titles, locations or unusual life events, and combinations of details can still reveal a participant.
  • Accuracy problems and loss of context in AI-processed data raise methodological concerns about how defensible the findings are.
  • Reviewing anonymised output line by line and planning for de-anonymisation risk are practical ways to reduce harm.
Apply it

To apply this, document how any AI tool handles your data, verify every transcript and anonymised file yourself, and use that record to justify your approach to your ethics board.

How to do it

  1. Review consent, ethics approval, data sensitivity, and whether the tool is authorized for the material.

    Review consent language, ethics approval, data sensitivity, participant expectations, and whether the tool is authorized. Identify excerpts that should never leave the approved environment.

  2. De-identify the dataset and remove unnecessary personal or contextual identifiers.

    De-identify the material with a written log and remove unnecessary contextual clues, not just names. Confirm that the resulting data still preserve the meaning needed for analysis.

  3. Create a preliminary human codebook or analytical framework before requesting AI suggestions.

    Create a preliminary human codebook or analytical framework before requesting AI suggestions. Define code meanings, inclusion boundaries, and examples so suggestions can be compared consistently.

  4. Compare AI-proposed codes with raw excerpts, negative cases, and the research team’s interpretation.

    Compare proposed codes with raw excerpts, negative cases, memo notes, and the research team’s interpretation. Do not accept frequency or semantic similarity as a substitute for contextual meaning.

  5. Document changes, unresolved disagreements, limitations, and how AI assistance was disclosed.

    Document changes to the codebook, unresolved disagreements, limitations, and the role of AI. Preserve reflexive notes and describe the assistance accurately in the final methods or disclosure.

Watch the tutorial35 min

Using AI in Qualitative Research Data Processing, EthicallyPlay video: Using AI in Qualitative Research Data Processing, Ethically

Chapters

  1. Welcome and speaker introduction
  2. How research ethics evolved: from informed consent to digital ethics
  3. What is qualitative data and why does it need extra care?
  4. Stages of qualitative data processing and outsourcing transcription
  5. Ethical questions to ask before commissioning transcription services
  6. AI transcription in video-call tools: bias and language limits
  7. What should informed consent say if you use AI to transcribe interviews?
  8. How accurate is AI transcription for research interviews?
  9. Is it safe to upload interview tutorials to online AI tools?
  10. Anonymisation vs pseudonymisation of qualitative data under GDPR
  11. Letting participants choose how their qualitative data is protected
  12. Can AI anonymise qualitative interview data?
  13. Can AI de-anonymise research data? Re-identification risks

Try this on SciSpace

Frequently asked questions

Is it ethical to use AI to transcribe qualitative interviews?

It can be, but only after you assess where the audio is stored, who can access it, and whether participants consented to third-party processing. Many ethics boards expect researchers to name the tool, explain its data handling, and check transcripts manually for errors. Treat AI transcription as a draft that you verify, not a finished record.

Can AI reliably anonymise qualitative research data?

Not on its own. Automated tools may miss indirect identifiers such as job titles, locations or unusual life events, and combining several details can still reveal a participant. Researchers should review anonymised output line by line and consider whether context makes someone recognisable. The tutorial explains why de-anonymisation is a real risk to plan for.

Who is this tutorial most useful for?

It suits qualitative and mixed-methods researchers, doctoral candidates preparing ethics applications, and research ethics committee members. Anyone who handles sensitive interview, focus-group or observational data and wants to understand the legal and methodological implications of AI processing before adopting it will find the guidance relevant.

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