  Following the recent surge in adoption of machine learning (ML), the negative
impact that improper use of ML can have on users and society is now also widely
recognised. To address this issue, policy makers and other stakeholders, such
as the European Commission or NIST, have proposed high-level guidelines aiming
to promote trustworthy ML (i.e., lawful, ethical and robust). However, these
guidelines do not specify actions to be taken by those involved in building ML
systems. In this paper, we argue that guidelines related to the development of
trustworthy ML can be translated to operational practices, and should become
part of the ML development life cycle. Towards this goal, we ran a multi-vocal
literature review, and mined operational practices from white and grey
literature. Moreover, we launched a global survey to measure practice adoption
and the effects of these practices. In total, we identified 14 new practices,
and used them to complement an existing catalogue of ML engineering practices.
Initial analysis of the survey results reveals that so far, practice adoption
for trustworthy ML is relatively low. In particular, practices related to
assuring security of ML components have very low adoption. Other practices
enjoy slightly larger adoption, such as providing explanations to users. Our
extended practice catalogue can be used by ML development teams to bridge the
gap between high-level guidelines and actual development of trustworthy ML
systems; it is open for review and contribution
