  While the operationalisation of high-level AI ethics principles into
practical AI/ML systems has made progress, there is still a theory-practice gap
in managing tensions between the underlying AI ethics aspects. We cover five
approaches for addressing the tensions via trade-offs, ranging from rudimentary
to complex. The approaches differ in the types of considered context, scope,
methods for measuring contexts, and degree of justification. None of the
approaches is likely to be appropriate for all organisations, systems, or
applications. To address this, we propose a framework which consists of: (i)
proactive identification of tensions, (ii) prioritisation and weighting of
ethics aspects, (iii) justification and documentation of trade-off decisions.
The proposed framework aims to facilitate the implementation of well-rounded
AI/ML systems that are appropriate for potential regulatory requirements.
