Data-centric Operational Design Domain Characterization for Machine
  Learning-based Aeronautical Products
Fateh Kaakai Shridhar "Shreeder" Adibhatla Ganesh Pai Emmanuelle Escorihuela
We give a first rigorous characterization of Operational Design Domains
(ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other
application sectors (such as self-driving road vehicles) where ODD development
is scenario-based, our approach is data-centric: we propose the dimensions
along which the parameters that define an ODD can be explicitly captured,
together with a categorization of the data that ML-based applications can
encounter in operation, whilst identifying their system-level relevance and
impact. Specifically, we discuss how those data categories are useful to
determine: the requirements necessary to drive the design of ML Models (MLMs);
the potential effects on MLMs and higher levels of the system hierarchy; the
learning assurance processes that may be needed, and system architectural
considerations. We illustrate the underlying concepts with an example of an
aircraft flight envelope.