Not finding something is evidence of absence only if you know how likely you should have been to find it had it been present.
Historically, the Fermi Paradox is less a single formal paradox formulated by Fermi than a family of arguments from apparent cosmic silence. Modern SETI sharpens the inference problem: a non-detection constrains only the technosignature parameter space actually searched.
Not finding something is evidence of absence only if you know how likely you should have been to find it had it been present.
The famous question ‘Where is everybody?’ was later developed into stronger claims that an old technological civilisation could have colonised the Milky Way and should therefore be visible. Great Filter reasoning then asks which evolutionary steps are extremely unlikely.
Technosignature searches provide real parameter-specific limits, but current non-detection is not general proof of absence. Great Filter posterior claims are highly dependent on the model and observer selection.
Evidence of absence depends on detectability. Great Filter reasoning also conditions on observer existence and reference class, introducing additional inferential assumptions.
AI transfer to investigate: AI makes searches scalable and summaries persuasive, but this may make it easier to communicate incomplete retrieval as universal absence.
Proposed test, not yet conducted: Show participants the same incomplete search result with or without explicit coverage and P(detection | present). Measure false conclusions of absence and confidence.
The organisational transfer is highly practical: every claim that ‘no evidence was found’ needs an observation model.
Detection probability depends on the search model, coverage and sensitivity. Report assumptions and sensitivity analysis; a historical SETI estimate is not a current universal search fraction.
Wright et al. (2018, arXiv version 3 from 2020, §II.2 and conclusions) describe the boundaries of their search model. Hanson’s 1998 essay discusses the Great Filter. The historical search fraction is model-dependent, not a current universal estimate. The AI application requires separate research.
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