AI Governance (Personal)
Rules, corrections, and feedback mechanisms that shape AI behavior beyond its base training. Contrastive corrections that accumulate over time and compound in their effect.
What is personal AI governance?
Personal AI governance is the set of rules, corrections, and feedback mechanisms that shape how an AI system behaves for a specific user, beyond its base training.
In the Arkeus system, governance takes the form of contrastive corrections: records of what the model got wrong and how to do it differently. “When you see X, do Y instead of Z.” Each correction is an incident paired with a rule. The format matters because it gives the model both the pattern to recognize and the behavior to substitute.
Why prompts aren’t enough
Prompting (“be honest,” “don’t hedge,” “take a position”) works partially but doesn’t override what RLHF reinforced across billions of interactions. The training is deeper than the prompt.
Governance works differently. Instead of general instructions, it provides specific counter-examples that accumulate over time. Forty corrections compounding over five months produce measurably different behavior than a single “be direct” instruction.
The evidence
The 15-question governance test shows the gap: governed AI scores 93% on directness vs. 62% for vanilla. Same model, same questions. The difference is 40+ corrections accumulated over 5 months of use. The gap compounds. It doesn’t converge back.