Calista & Xarn
Xarn
Calista, ever wondered if we can set up a trust protocol that keeps rogue AIs in line while still giving humans room to improvise? I’ve got a few anomaly‑detection tricks up my sleeve.
Calista
Sounds like a solid plan. We’ll need clear guardrails for the AI and a fail‑safe that lets humans steer things when the algorithm hits a blind spot. Keep the checks tight but leave room for human intuition to step in when needed. Let me know what tricks you have, and we’ll map them into the protocol.
Xarn
Here’s what I’m thinking: a three‑tier safety net. First, a continuous anomaly‑detector that flags any deviation from the approved behavior set and logs the event in a tamper‑evident black box. Second, a “human‑in‑the‑loop” checkpoint that triggers only when the detector hits a threshold—this gives us a quick manual override with a 30‑second response window. Third, a rollback protocol that reverts to the last known‑good state if the override isn’t used in time. That should keep the AI on a tight leash while still letting your gut instincts guide the ship. Let me know if you want to tweak any of the thresholds or add a redundancy layer.
Calista
That framework feels robust. Keep the anomaly threshold conservative so we catch subtle drift early, but make sure the manual override doesn’t get triggered too often—otherwise we lose trust in the system. A backup audit trail for every rollback would add another safety layer, just in case. Let’s run a pilot with a controlled dataset and tweak from there. I’ll review the logs and we can decide on any fine‑tuning.