  As the range of potential uses for Artificial Intelligence (AI), in
particular machine learning (ML), has increased, so has awareness of the
associated ethical issues. This increased awareness has led to the realisation
that existing legislation and regulation provides insufficient protection to
individuals, groups, society, and the environment from AI harms. In response to
this realisation, there has been a proliferation of principle-based ethics
codes, guidelines and frameworks. However, it has become increasingly clear
that a significant gap exists between the theory of AI ethics principles and
the practical design of AI systems. In previous work, we analysed whether it is
possible to close this gap between the what and the how of AI ethics through
the use of tools and methods designed to help AI developers, engineers, and
designers translate principles into practice. We concluded that this method of
closure is currently ineffective as almost all existing translational tools and
methods are either too flexible (and thus vulnerable to ethics washing) or too
strict (unresponsive to context). This raised the question: if, even with
technical guidance, AI ethics is challenging to embed in the process of
algorithmic design, is the entire pro-ethical design endeavour rendered futile?
And, if no, then how can AI ethics be made useful for AI practitioners? This is
the question we seek to address here by exploring why principles and technical
translational tools are still needed even if they are limited, and how these
limitations can be potentially overcome by providing theoretical grounding of a
concept that has been termed Ethics as a Service.
