  The right to AI explainability has consolidated as a consensus in the
research community and policy-making. However, a key component of
explainability has been missing: extrapolation, which describes the extent to
which AI models can be clueless when they encounter unfamiliar samples (i.e.,
samples outside the convex hull of their training sets, as we will explain). We
report that AI models extrapolate outside their range of familiar data,
frequently and without notifying the users and stakeholders. Knowing whether a
model has extrapolated or not is a fundamental insight that should be included
in explaining AI models in favor of transparency and accountability. Instead of
dwelling on the negatives, we offer ways to clear the roadblocks in promoting
AI transparency. Our analysis commentary accompanying practical clauses useful
to include in AI regulations such as the National AI Initiative Act in the US
and the AI Act by the European Commission.
