  Assessing the trustworthiness of artificial intelligence systems requires
knowledge from many different disciplines. These disciplines do not necessarily
share concepts between them and might use words with different meanings, or
even use the same words differently. Additionally, experts from different
disciplines might not be aware of specialized terms readily used in other
disciplines. Therefore, a core challenge of the assessment process is to
identify when experts from different disciplines talk about the same problem
but use different terminologies. In other words, the problem is to group
problem descriptions (a.k.a. issues) with the same semantic meaning but
described using slightly different terminologies.
  In this work, we show how we employed recent advances in natural language
processing, namely sentence embeddings and semantic textual similarity, to
support this identification process and to bridge communication gaps in
interdisciplinary teams of experts assessing the trustworthiness of an
artificial intelligence system used in healthcare.
