[cs.LG] 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 likely to yield significant false positives.
Ankit Gupta, Christoph Adami (Michigan State University)
Comments: 8 pages, 7 figures. Proceedings of Alife 2026
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Populations and Evolution (q-bio.PE)
Cite as: arXiv:2604.11915 [cs.LG] (or arXiv:2604.11915v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.11915
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Submission history
From: Christoph Adami
[v1] Mon, 13 Apr 2026 18:05:57 UTC (152 KB)
[v2] Mon, 22 Jun 2026 17:37:37 UTC (154 KB)
https://arxiv.org/abs/2604.11915
Astrobiology,
