Ethics as a Service: A Pragmatic Operationalisation of AI Ethics

Abstract

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.’

Operationalising AI ethics: barriers, enablers and next steps

“…digic atapu lt. org. uk/ for-start ups/ other-progr ammes/ appli ed-ai-ethics-typol ogy); and the formalisation of the concept 'Ethics as a Service' (Morley et al 2021). Whilst the findings from this previous research have proven useful, for the sake of simplicity we have 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.…”

A Code of Digital Ethics: laying the foundation for digital ethics in a science and technology company

“…The level of reference, underlying principles and recommended measures or organisational elements were frequently not easily identifiable. Indeed, the translation of "lofty" principles (Mittelstadt 2019) into robust and operationalisable guidelines that can enable truly informed decisions on the ground has been identified as a key challenge for digital ethics (Floridi 2019;Blackman 2020;Morley et al 2021a). We decided to tackle this challenge with a strong dual focus on clarity and operationalisability.…”

AI Ethics Principles in Practice: Perspectives of Designers and Developers

“…The operationalisation of AI ethics can be framed as a design process rather than an end goal [17], [18]. Fostering an understanding of the complexities and challenges of implementing responsible AI provides the foundation for iterative and adaptive approaches rather than as the completion of activity checklists.…”