As AI moves from "chatbots" to "agents" that can execute tasks independently, the stakes for alignment have never been higher. In our latest blog post, our Ethics & Safety team explores the "Incentive Drift" phenomenon.
We discuss why traditional Reinforcement Learning from Human Feedback (RLHF) might fail as agents gain more autonomy and propose a new "Constitutional Check" layer that acts as a real-time safety guardrail.
Key Takeaways:
Why "intent" is harder to measure than "output."
The role of human-in-the-loop (HITL) in long-running tasks.
Building transparent audit logs for AI decisions.