Modern machine learning methods have been proposed to detect life in extraterrestrial samples, drawing on their ability to distinguish biotic from abiotic samples based on training models using natural and synthetic organic molecular mixtures. Here we show using Artificial Life that such methods are easily fooled into detecting life with near 100% confidence even if the analyzed sample is not capable of life. This is due to modern machine learning methods' propensity to be easily fooled by out-of-distribution samples. Because extra-terrestrial samples are very likely out of the distribution provided by terrestrial biotic and abiotic samples, using AI methods for life detection is bound to yield significant false positives.

翻译：现代机器学习方法已被提议用于探测地外样本中的生命，其依据是通过利用天然与合成有机分子混合物训练模型，从而区分生物与非生物样本的能力。本文通过人工生命研究表明，即便分析样本不具备生命能力，此类方法也极易以近乎100%的置信度误判存在生命。这是由于现代机器学习方法容易受分布外样本的欺骗。由于地外样本极有可能偏离由地球生物与非生物样本构成的分布，采用人工智能方法进行生命探测必然导致显著的假阳性结果。