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Can AI Detect Life? Lessons from Artificial Life

arXiv 2604.11915 ↗First posted 13 Apr 2026cs.LG cs.AI cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 22 references that are in the map. First posted on arXiv 13 Apr 2026.

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4 of 22 references.

References · 4

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  4. 2025SPOOF: Simple Pixel Operations for Out-of-Distribution Foolingcited by 1

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