Actually, this isn't as "obvious" as it seems—it’s a classic case of contextual bias.
We only view these answers as "wrong" because we reflexively fill in missing data with our own personal experiences. For example:
- You might be parked 50m away and simply hand the keys to an attendant.
- The car might already be at the station for detailing, and you are just now authorizing the wash.
This highlights a data insufficiency problem, not necessarily a logic failure. Human "common sense" relies on non-verbal inputs and situational awareness that the prompt doesn't provide. If you polled 100 people, you’d likely find that their "obvious" answers shift based on their local culture (valet vs. self-service) or immediate surroundings.
LLMs operate on probabilistic patterns within their training data. In that sense, their answers aren't "wrong"—they are simply reflecting a different set of statistical likelihoods. The "failure" here isn't the AI's logic, but the human assumption that there is only one universal "correct" context.