AI methods proposed for detecting extraterrestrial life are highly vulnerable to false positives when encountering samples outside their training data

Machine learning models trained on Earth-based organic samples can achieve near 100% false positive rates when analyzing unfamiliar extraterrestrial materials

The vulnerability stems from AI's inability to handle out-of-distribution samples, a critical limitation when analyzing truly alien compounds

Artificial Life experiments demonstrate that current detection methods mistake non-living samples for biological ones with dangerous reliability

Extraterrestrial samples will almost certainly fall outside the distribution of terrestrial training data, making current AI approaches unreliable for space exploration missions