  Processing fragments of data collected on a monitored person to find out
whether this person is a would-be terrorist (WT) is very challenging. Moreover,
the process has proven to be deceptive, with repeated dramatic failures. To
address the issue I suggest a mirror simple model to mimic the process at
stake. The model considers a collection of ground items which are labelled
either Terrorist Connected (TC) or Terrorist Free (TF). To extract the signal
from the ground data items I implement an iterated coarse-grained scheme, which
yields a giant unique item with a label TC or TF. The results obtained validate
the processing scheme with correct outcomes for the full range of proportions
of TC items, beside in a specific sub-range. There, a systematic wrong
labelling of the giant item is obtained at the benefit of WT, who are wrongly
labeled not would-be terrorist (NWT). This flaw proves to be irremovable
because it is anchored within the processing itself in connexion with the
treatment of uncertain aggregates, which inevitable appear. The ``natural"
allocation of uncertain aggregates to the TF label, in tune with the ethical
application of the presumption of innocence in force in democracies, confines
the wrong diagnosis systematically to the benefit of some WT, who are labelled
NWT. It happens that applying the presumption of guilt, instead of that of
innocence, to label the indeterminate aggregates, shifts the systematic errors
to another sub-range of proportions of TC items with then zero errors in
identifying a WT. But instead, some NWT are wrongly labeled WT. Despite of
being quite far from reality, my results suggest that intelligence agencies
should investigate the overall effect of seemingly inconsequential "natural"
labelling, which are applied locally when data are uncertain with no immediate
detectable impact on the data processing.
