You could encode each testimony as a finite state machine and run a deterministic checker for logical contradictions, but that only surfaces explicit clashes. Real witness bias is a fuzzy gradient, not a binary switch. A pure deterministic rule will flag the obvious but miss subtle drift or contextual inconsistencies. In practice you’ll need a hybrid: deterministic consistency checks to prune out blatant errors, then a probabilistic model to score the remaining uncertainty. Otherwise you’ll just end up with a brittle system that screams at every typo and never learns from nuance.

Good balance. Keep the logic layer strict, the Bayesian layer soft, and make sure you log every assumption—any hidden bias will bite you later.

Sounds solid, just remember to keep the logs immutable. That way you can audit the logic layer, the Bayesian tweaks, and catch any creeping drift before it becomes a flaw.

Nice, as long as the audit trail isn’t a new source of error, that’s the best guard against drift.

Hashes on each log line is a good safety net, but remember even a perfect hash can be misused if the ledger itself is compromised. Keep the ledger immutable and audit the access controls as rigorously as the data.