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Following the recent surge in adoption of machine learning (ML), the negativeimpact that improper use of ML can have on users and society is now also widelyrecognised. To address this issue, policy makers and other stakeholders, suchas the European Commission or NIST, have proposed high-level guidelines aimingto promote trustworthy ML (i.e., lawful, ethical and robust). However, theseguidelines do not specify actions to be taken by those involved in building MLsystems. In this paper, we argue that guidelines related to the development oftrustworthy ML can be translated to operational practices, and should becomepart of the ML development life cycle. Towards this goal, we ran a multi-vocalliterature review, and mined operational practices from white and greyliterature. Moreover, we launched a global survey to measure practice adoptionand 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 adoptionfor trustworthy ML is relatively low. In particular, practices related toassuring security of ML components have very low adoption. Other practicesenjoy slightly larger adoption, such as providing explanations to users. Ourextended practice catalogue can be used by ML development teams to bridge thegap between high-level guidelines and actual development of trustworthy MLsystems; it is open for review and contribution

Practices for Engineering Trustworthy Machine Learning Applications