Can Research Interview Transcripts Be Uploaded to Generative AI?

Can You Paste or Upload Research Interviews Into Generative AI?

Generative AI can summarize an interview, suggest preliminary codes, compare participants, extract statements about a topic, translate responses, or help researchers explore patterns across qualitative data. It is easy to see why uploading a folder of transcripts is tempting.

Interview transcripts, however, are not ordinary text documents. They may contain identifiable participant information, sensitive disclosures, information about people who never consented to participate, and contextual details that reveal identity even after names have been removed.

The question is therefore not simply whether generative AI can analyze interviews. It is whether your particular transcripts may be disclosed to and processed by the particular AI service you intend to use.

Interview Transcripts Should Not Be Uploaded by Default

Why Interview Transcripts Require Particular Care With Generative AI

A Transcript Can Be Identifying Without Containing a Name

Qualitative interviews are designed to preserve context. That is precisely what makes them analytically rich, and precisely what can make them difficult to anonymize.

A participant may describe where they work, the position they hold, an unusual event they experienced, a family member, a diagnosis, a dispute with a colleague, a specific date, or a sequence of events recognizable to people familiar with their community. A quotation itself may also be searchable or distinctive.

The UK Information Commissioner's Office specifically notes that anonymizing qualitative material can be time-consuming and may require careful human judgment. Its guidance uses interview transcripts as an example and describes removing directly identifying information, generalizing potentially identifying details, and considering additional privacy-preserving transformations.

This is why de-identifying participant data for AI use cannot be reduced to running find-and-replace on participant names.

Interview Participants Often Talk About Other People

A transcript may contain personal information about individuals who are not research participants at all.

An employee interviewed about workplace culture may identify a supervisor and describe alleged misconduct. A patient may discuss a spouse's medical condition. A teacher may name a student. A participant discussing family experiences may reveal sensitive information about relatives.

Your participant's consent does not automatically constitute consent on behalf of every person mentioned during the interview. Whether those third-party references constitute personal data or create confidentiality concerns depends on their content and the applicable framework, but they should not be ignored merely because the study's participant has agreed to participate.

Verbatim Quotations Can Function as Identifiers

Researchers often value verbatim quotations because they preserve participants' voices and provide evidence for qualitative interpretations. The same feature can complicate confidentiality.

A distinctive quotation may be recognized by colleagues, family members, community members, or anyone who has encountered the same statement elsewhere. If the participant has previously posted similar wording publicly, exact or near-exact text may also facilitate linkage.

For AI processing, ask whether the system genuinely requires the complete verbatim transcript. Some tasks may be possible with carefully selected, de-identified excerpts. Others may depend on linguistic detail and therefore require more of the original text. That trade-off should be made deliberately rather than resolved by uploading everything.

Replacing Names With Codes Is Usually Not Enough

Changing “Professor Santos” to “Participant 14” can reduce direct identification, but the transcript may still say that Participant 14 is the only female dean at a particular institution, lives in a named municipality, completed a doctorate at a particular university, and led the institution during a widely reported event.

The identification problem has simply moved from one sentence to the rest of the transcript.

Researchers should therefore assess whether contextual clues could allow supposedly de-identified participants to be re-identified. Free text deserves particular attention because identifying information can be expressed in countless linguistic forms.

Consent for an Interview Does Not Automatically Mean Consent for Every Processing Method

Participants may have agreed to be interviewed and to have their responses analyzed for a research project. Whether that consent also covers processing the transcript through an external generative AI service depends on what participants were told, the approved research arrangements, the nature of the AI processing, and applicable institutional and legal requirements.

A consent form that states “the research team will analyze your interview” does not necessarily answer whether an external provider may receive the transcript. Conversely, the mere absence of the words “artificial intelligence” does not automatically establish that every AI-assisted use is prohibited. Context matters.

Researchers should examine whether informed consent needs to address AI processing rather than inferring permission from generic language.

Your Ethics Protocol May Describe How Transcripts Will Be Handled

Approved research protocols often specify how interviews will be recorded, transcribed, stored, accessed, de-identified, analyzed, and eventually destroyed or archived. Introducing an external generative AI platform may change those arrangements.

If your approved protocol says transcripts will remain on an encrypted institutional drive accessible only to named research personnel, uploading those transcripts to an external service is not obviously equivalent to analyzing them with locally installed software.

Where AI was introduced after ethics approval, determine whether the change requires an ethics amendment or another institutional determination before proceeding.

The AI Product and Account Type Matter

“Generative AI” is not a single data environment. Consumer services, educational workspaces, enterprise systems, APIs, locally hosted models, and institutionally managed deployments can have different retention, training, security, access, and contractual arrangements.

For example, OpenAI currently states that inputs and outputs from ChatGPT Business, Enterprise, Edu, Healthcare, Teachers, and its API platform are not used to train its models by default. It also offers data-retention and residency controls for qualifying products and organizations. Those conditions differ from personal ChatGPT workspaces, where model-improvement settings and other data controls operate differently.

This is an illustration of why researchers must verify the exact service they intend to use rather than conclude that a provider is either universally “safe” or universally “unsafe.” Provider protections also do not override your institution's rules or your study's ethics and consent arrangements.

AI Output Can Introduce a Different Kind of Confidentiality Problem

Researchers understandably focus on what they send to an AI system, but the generated output also deserves attention. A model asked to summarize several interviews may reproduce sensitive details, combine information from different participants, or create an inaccurate statement that appears to describe a real participant.

Outputs should therefore remain within the appropriate research environment and be treated according to their content. A generated thematic summary containing participant-specific information does not become non-sensitive merely because the AI wrote it.

AI-Assisted Qualitative Analysis Still Requires Methodological Accountability

Privacy is not the only concern. If generative AI proposes codes, themes, interpretations, or summaries, researchers remain responsible for determining whether those outputs accurately represent the data and fit the chosen analytic methodology.

A language model may collapse distinctions, overlook negative cases, impose categories not grounded in the participants' accounts, or produce plausible interpretations unsupported by the transcript. These are methodological issues rather than reasons to prohibit AI categorically.

AI assistance should therefore be incorporated into a transparent analytic workflow in which researchers can explain what the system did, what material it processed, how outputs were evaluated, and where human analytical judgment remained necessary.

What to Check Before Uploading an Interview Transcript

Common Mistakes When Putting Interview Transcripts Into AI

Do Not Make the Full Transcript Your Default Unit of Disclosure

Before sending an interview to generative AI, ask what the intended task actually requires. A complete verbatim transcript may be necessary for some analyses, but many tasks can be performed with less information.

If you want help refining a coding framework, for example, synthetic excerpts may be enough during early experimentation. If you need assistance examining a specific passage, the model may not need every interview. If the research requires whole-transcript comparison, stronger controls may be justified, but the need should be explicit.

The same principle applies beyond text. If your qualitative data remain in their original form, separate issues arise when participant audio or video recordings are processed with AI, because voice, face, background information, and other features may themselves be identifying.

Before Uploading an Interview Transcript to Generative AI

Questions About Interview Transcripts and Generative AI

Interview Transcripts Need More Than a Quick Name Removal

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