Whitepaper
AI with survey data, GDPR-proof
What language models do well with survey data, where they fail, and how to approve them without data leaking out
What you get
- Four AI tasks that work with survey data, and the one that reliably fails
- Open-ended answers as a variable instead of a summary: codeframe, residual category, sample check
- What goes to the model when you ask a question of the dataset, and what never does
- Four operating models from US API to on-premise in one table with six criteria
- A test protocol that measures AI coding like a human coder
- A 15-question checklist for approval by data protection and IT
In short
A language model writes, a dataset computes. Whoever keeps to this division of labour gets four things from survey data with AI, reliably: coded open-ended answers, a sentiment per response, a questionnaire draft and an answer to a question put to the weighted dataset. Whoever ignores it and hands a model a PDF to summarise gets invented percentages. This whitepaper describes the use cases that hold up, names the data that flows to a model in each of them, compares four operating models from a US API to your own server, and closes with a 15-question checklist with which data protection and IT can grant approval or refuse it with reasons.
- Who it is for
- Institutes & agencies · Corporate insights teams · BI & data teams
- Format
- PDF, 14 pages, with worksheets and checklists
Contents 9 chapters
- 01 What AI can do with survey data, and what it cannot
- 02 Coding open-ended answers: the most honest use case
- 03 Querying data instead of reading reports
- 04 The data protection question, asked properly
- 05 Four operating models compared
- 06 Synthetic respondents and AI interviews: a classification
- 07 Measuring quality: checking AI coding like a human coder
- 08 Checklist for approval by data protection and IT
- 09 Where DataLion fits in
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