  Concerns about the societal impact of AI-based services and systems has
encouraged governments and other organisations around the world to propose AI
policy frameworks to address fairness, accountability, transparency and related
topics. To achieve the objectives of these frameworks, the data and software
engineers who build machine-learning systems require knowledge about a variety
of relevant supporting tools and techniques. In this paper we provide an
overview of technologies that support building trustworthy machine learning
systems, i.e., systems whose properties justify that people place trust in
them. We argue that four categories of system properties are instrumental in
achieving the policy objectives, namely fairness, explainability, auditability
and safety & security (FEAS). We discuss how these properties need to be
considered across all stages of the machine learning life cycle, from data
collection through run-time model inference. As a consequence, we survey in
this paper the main technologies with respect to all four of the FEAS
properties, for data-centric as well as model-centric stages of the machine
learning system life cycle. We conclude with an identification of open research
problems, with a particular focus on the connection between trustworthy machine
learning technologies and their implications for individuals and society.
