Research Privacy & Ethics in the Age of AI
A ten-point diagnostic worksheet that shows where participant data is safe, where it's exposed, and what to fix first.
What's included
A ten-point maturity assessment. Score your practice from 0 to 3 across ten areas where AI creates new privacy and ethics risk, with clear descriptions of what each level looks like in practice.
A scorecard with a clear next step. Total your score out of 30 and see where you land: Critical Gaps, Prioritize Improvements, or Operational Excellence. Each band comes with specific actions.
A meeting-ready process. A four-step approach for running the assessment with research, legal, InfoSec, and procurement in the same room, so the conversation starts with evidence instead of opinions.
A plain-language glossary. Shadow AI, zero data retention, subprocessors, differential exclusion, and more, defined so everyone at the table is working from the same vocabulary.
A curated reading list. Key references from NIST, the EU AI Act, academic research, and UX practitioner literature.
Who this resource is for
ResearchOps leads, research managers, and anyone who builds, manages, or governs research systems. It works for a team of three or an enterprise research org, and applies across qualitative and quantitative research.
Use it to:
- See where your practice stands today across all ten areas
- Find gaps in both privacy controls and research ethics
- Walk into your next legal or security review prepared
- Separate what individuals are responsible for from what your systems should handle
