  The ability to identify and control different kinds of linguistic information
encoded in vector representations of words has many use cases, especially for
explainability and bias removal. This is usually done via a set of simple
classification tasks, termed probes, to evaluate the information encoded in the
embedding space. However, the involvement of a trainable classifier leads to
entanglement between the probe's results and the classifier's nature. As a
result, contemporary works on probing include tasks that do not involve
training of auxiliary models. In this work we introduce the term indicator
tasks for non-trainable tasks which are used to query embedding spaces for the
existence of certain properties, and claim that this kind of tasks may point to
a direction opposite to probes, and that this contradiction complicates the
decision on whether a property exists in an embedding space. We demonstrate our
claims with two test cases, one dealing with gender debiasing and another with
the erasure of morphological information from embedding spaces. We show that
the application of a suitable indicator provides a more accurate picture of the
information captured and removed compared to probes. We thus conclude that
indicator tasks should be implemented and taken into consideration when
eliciting information from embedded representations.
