Operationalising AI ethics: barriers, enablers and next steps

TLDR

Although more than 80 AI ethics guides existed by 2019, the field remains abstract and its practical relevance to designers is limited, and it is unclear whether practitioners are aware of or actively addressing ethical implications. This study builds on a prior typology of tools that translate AI ethics principles into design practices and seeks to close the gap by conducting a mixed‑methods qualitative analysis of practitioners’ understanding, motivation, barriers, and assistance needs. The authors developed a searchable typology linking five core AI ethics principles to implementable design practices and applied mixed‑methods qualitative methods to investigate practitioners’ perspectives.

Abstract

Abstract By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that all AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice?