Any personal information you provide when using LiminalX — your name, email address — belongs to you. We will never share, sell, or transfer your personal data to any third party without your specific, informed consent for that specific purpose.
You can request the deletion of your account and all associated personal data at any time, for any reason. Submit a request through the platform or contact us directly at [email protected] and we will action it promptly — no questions, no friction. We are working on a method for you to directly delete your data with no email — standby.
Deletion is complete. We do not retain shadow copies or archived versions of your personal records after a deletion request has been fulfilled.
We ask for your permission to use fully anonymised and aggregated data to understand how AI is being adopted across our healthcare community. This is the core of what makes LiminalX useful beyond the individual — it lets us see where the field is, and build empowerment offerings that respond to real needs rather than assumptions.
You will never be identifiable in any aggregated analysis. We enforce minimum cohort thresholds architecturally — no output is ever produced from a group small enough to risk identification. Your data contributes to a collective picture; it is never presented in a way that could be traced back to you.
This permission is separate from your account and is always opt-in. You can withdraw it at any time.
When you participate in a live LiminalX workshop, your anonymised assessment responses are aggregated with those of other attendees to produce real-time cohort insights — the kind of honest, collective picture that makes in-person sessions genuinely useful. All participants in the workshop see the same aggregated results; no individual responses are visible to anyone.
This cohort data is used within the workshop session. You will be clearly informed of this at the point of participation, and your consent will be obtained before any data collection begins. The same anonymisation standards apply — you cannot be identified from cohort outputs, and we enforce minimum group thresholds to ensure this architecturally, not just as policy.