The AI Moral Code

As AI systems transition from lab to real-world environments, they are exposed to unpredictable inputs, emergent behaviors, and unanticipated feedback loops. These can result in what we call moral drift—a slow deviation from initial ethical intentions or policy constraints.

This phenomenon often arises due to distributional shift, unsupervised learning adaptation, or misaligned incentives in feedback mechanisms. Over time, the system may begin to act in ways that are technically correct but ethically misaligned.

Moral drift highlights the importance of continuous monitoring, ethical re-alignment protocols, and post-deployment audits. In the NRBC model, this falls under Behavioral (B) mechanisms and must be reinforced through Regulatory (R) and Conceptual (C) redundancies.

By embedding ethical resilience and revalidation checkpoints, we can address the long tail of AI behavior in the wild—before it undermines trust or causes harm.