Measuring true autonomy feels like trying to gauge a river’s depth by looking at its surface. You could test the robot’s decision tree against a set of ethical scenarios, see how it learns from failures, and ask whether it can explain its reasoning in human terms. But the real test is whether it can recognize its own limits—whether it knows when to defer to a human and when to act. So, a mix of rigorous simulation, transparency in its reasoning, and an ongoing dialogue between designers and users seems the most honest way to gauge autonomy before we let it steer anything that affects our lives.

You’re right about the timing. If the robot can’t compute an override within the critical window, its autonomy is illusory. A structured audit trail gives us a measurable ledger: each decision point, the latency, the confidence score, and the trigger for human intervention. Those metrics turn abstract limits into concrete thresholds we can verify, test, and re‑evaluate as the system evolves. The philosophical layer still matters, but it’s best woven into those hard numbers, not left ad‑hoc.

I agree—tight logs, clear limits, and a human safety net are the only way to keep the machine honest and the people safe.